Research Data
- What is research data?
- What is research data management?
- Why should research data management be important to me?
- What do I have to keep in mind when planning my project?
- How do I create a good data management plan?
- Which specific demands do sponsors, publishers, and universities have?
Data Storage and Digital Preservation
- How can I structure my data?
- Which file formats should I choose?
- Where do I store my data during my work process?
- What should I consider when backing up my data?
- Where can I archive my data on a long-term basis?
Publishing and Sharing Research Data
- Why should I publish my data?
- Is there any resaon against publishing?
- Which restrictions by data protection laws do I have to consider?
- Who can decide on whether to share or publish data?
- Do I own the copyright to my data?
- Can I control the use of my data?
- Which license should I choose?
- How can I publish my data?
- How can I find a suitable repository?
- What do I have to consider when uploading data into a repository?
- What are Metadata, Metadata Schema, Controlled Vocabularies and Documentations?
- What are Persistent Identifiers?
Introduction
What is research data?
The term “research data” generally refers to all kinds of (digital) data that represent the result of scientific work or that serve as a basis for such work. Research data is generated using a wide variety of methods, such as measurements, source research or surveys. Therefore, it is always subject- and project-specific. For additional information on defining research data, see here .
What is research data management?
Research data management includes measures that create and preserve sustainable data. Thus, it is relevant throughout the entire data lifecycle ( fig. 1 ).
Fig. 1: Research data lifecycle
Ideally, such planning commences at the beginning of a research project and is regularly updated. Research data management does not only refer to data storage and archiving. It also enables you to find, access and comprehend data, and – as a result – use your data well into the future. For further information on research data management see here .
Why should research data management be important to me?
There are several good reasons for systematically tackling research data management, which likewise stress the importance of good scientific practice:
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Research funding: Research funders often require research data management, partly also data sharing. Both are to validate research results and prevent multiple funding. ( Which specific demands do sponsors, publishers, and universities have? ) |
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Reusability: Good research data management minimizes the risk of losing data. The extraction of data is usually time-consuming and cost-intensive. Research data management ensures that well-prepared and documented research data remains permanently usable and reusable – for even longer than the ten years required by good scientific practice. |
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Reproducibility : Long term-reproducibility is ensured by appropriately maintaining experimentally obtained research data. |
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Verifiability: Results are verifiable in the long term by documenting research data and their origin. |
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Citability: Data publications are citable as independent publications and thus increase the visibility of your own research. |
In the figure below, you can see different possible aims of research data management:
Fig. 2: Aims of research data management
What do I have to keep in mind when planning my project?
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Ideally, you do not start thinking about research data management only after you have already gathered your data. Rather, it is best to consider how you further want to deal with your data before you create it. Thus, you might use the data lifecycle for orientation. Kassel University’s guidelines (Ger) on what to consider when managing research data can give you an overview: |
- Appoint the people responsible for setting up and controlling research data management at your institution.
- Check whether there are institute- and discipline-specific or general requirements and recommendations on how to handle research data.
- Always check which requirements on archiving and publishing research data you have to meet as early as possible. ( Which specific demands do sponsors, publishers, and universities have? )
- Check which research data is collected during your research project.
- Think about which research data is to be published and provided for reuse.
- Think about how to store and archive your research data. ( Data storage and digital preservation )
- What options do you have for storing and archiving research data? Could you use a generalist or discipline-specific repository? ( How can I find a suitable repository? )
- Clear up all legal questions on storing and sharing research data. You might have to consider data protection and copyright.
- Create a data management plan to document your decisions. It will also serve as validation of your progress and project implementation. ( How do I create a good data management plan? )
- Update your data management plan regularly during the course of your research.
How do I create a good data management plan?
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A data management plan records the handling of your research data all the way from the planning stage to the completion of your research project. It is to be understood as a "living document" which, if necessary, can and must be adapted to changes, new findings or problems. To create your data management plan, these tools might be useful:
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Research Data Management Organiser by the Leibniz Institute for Astrophysics Potsdam is currently in beta phase. The aim of the project is not only to support you in creating data management plans according to the needs of research funders, but also to guide you in planning, executing and maintaining your research data management.
Is run by the British Digital Curation Centre (DCC) and thus mainly geared to the needs of British researchers. However, it can also be used for Horizon 2020-projects, for which Humboldt University of Berlin has provided
guidelines
I s operated by the California Digital Library. The website also offers examples for data management plans. Because of the different funding schemes, it is only of limited use for German/European projects .
ARGOS is a European online tool developed by OpenAIRE to create data management plans, based on the open source software OpenDMP. The registration process is designed to be very open, as you can log in directly with your ORCiD ID, Google account, or even Facebook or Twitter account. Data management plans created with ARGOS are also internationally interchangeable, as they are directly available in the DMP metadata standard of the Research Data Alliance (RDA).
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There are also several checklists, templates and wizards which can give you further aid in creating your data management: Checklists :
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Examples and Templates:
- DMP Catalogue of the LIBER Research Data Management Working Group
- Exemplary-DMP for BMBF (Federal Ministry of Education and Research) proposals: BMBF proposal (Ger)
- Exemplary-DMP for DFG (German Research Foundation) proposals: DFG proposal (Ger)
- Science Europe [2021]: Practical Guide to the International Alignment of Research Data Management (DMP Template from page 15)
- DMP Template for Horizon Europe (in .docx as download)
- Search on Zenodo.org with Keyword "DMP"
- DMP Template RWTH Aachen (Ger)
Wizards:
- CLARIN-D (Common Language Resources and Technology Infrastructure)
- KomFor (The centre of competence for research data in the earth and environmental science)
Exemplary Data Management Plans:
- Public DMPs using DMPTool
- DCC - Example DMPs and guidance
- University of Leeds - DMP tools and examples
- UC San Diego - Sample NSF DMPs
There is also Humboldt-University’s video tutorial (Ger) to give you a brief introduction to DMPs.
Which specific demands do sponsors, publishers, and universities have?
- Deutsche Forschungsgemeinschaft (DFG) (= German Research Foundation)
In its “Proposals for Safeguarding Good Scientific Practice “ (Eng + Ger) the DFG states that “ Primary data as the basis for publications shall be securely stored for ten years in a durable form in the institution of their origin“. It goes on with:
“In the interest of transparency and to enable research to be referred to and reused by others, whenever possible researchers make the research data and principal materials on which a publication is based available in recognised archives and repositories in accordance with the FAIR principles ( F indable, A ccessible, I nteroperable, R eusable).”
In 2015, the DFG Guidelines on the Handling of Research Data were passed. They state further recommendations on providing data and planning data driven projects, like:
„Applicants should consider during the planning stage whether and how much of the research data resulting from a project could be relevant for other research contexts and how this data can be made available to other researchers for reuse. Applicants should therefore detail in the proposal what research data will be generated or evaluated during a scientific research project. Concepts and considerations appropriate to the specific discipline for quality assurance and the handling and long-term archiving of research data should be taken as a basis.“
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European Commission (EC)
The EC’s “Open Research Data Pilot” is part of the EU research and innovation program Horizon 2020. Its aim is to improve the access and reusability of research data originating in Horizon 2020 projects. The Open Research Data Pilot’s basic principle is the motto “as open as possible, as restricted as necessary”. ( EC Guidelines on FAIR Data Management in Horizon 2020 , p. 4)
During 2014-2016, only selected aspects of Horizon 2020 have been included into the project, but since the revised 2017 version of the program was released, all aspects are covered now.
There are three main obligations:
You have to create a data management plan according to the template. It has to be handed in within the first six months and updated according to relevant adjustments (or at least at interim and final evaluations).
Data storage : Your research data has to be stored in an institutional, project-specific or discipline-specific data repository as early as possible (‘underlying data’) or according to the data management plan (‘other data’).
Publication : If possible, your data should be published using an open license (preferably CC-BY or CC-O) without use restrictions. The publication has to include the necessary contextual information and tools.
However, if there are legitimate reasons, a partial or complete waiver of these requirements is possible
You can find further information by looking at the following:
Guidelines on FAIR Data Management in Horizon 2020
Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020
Horizon 2020 Online Manual: Open Access and Data Management
Horizon 2020: Annotated Model Grant Agreement (AGA)
OpenAIRE Research Data Management Briefing Paper
- Publishers
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Publishers increasingly demand you provide the research data a publication is based upon. Therefore, you should always check these requirements before you publish your work. Examples for guidelines can be found here: |
Public Library of Science (PLOS): Data Availability Policy / Materials and Software Sharing Policy
Nature Publishing Group: Availability of Data, Material and Methods Policy
Science: Data and Materials Availability Policy and Preparing Your Supplementary Materials
BioMed Central: Availability of Supporting Data
Elsevier: Text and Data Mining; Research Data Policy
- Justus-Liebig-University Giessen
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On 29 th October 2018 Justus-Liebig-University Giessen has given itself research data guidelines ( Research Data Policy ) (Ger). These guidelines define the principles on which members of the university are to handle research data. |
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Data Storage and Digital Preservation
How can I structure my data?
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Often, not only a multiplicity of data sets, but also different versions of your data are created during the work process. For efficient and collaborative workflows, you should decide on specific conventions for naming and versioning your data. Furthermore, this will enable the long-term traceability and usability of your data. Moreover, it can be useful to define separate directory structures for raw data, analysis data, data evaluations and any other project materials. For further information on file organization see Jensen 2012, pp. 40-42 (Ger) and Christian Krippes: Coffee Lecture on File and Data Organization (Ger). |
At the different stages of modifying your data (e.g. raw data, cleaned data, data ready for analysis) you should create write-protected versions. You should only use copies of those original files for further processes.
Naming conventions can vary widely depending on your discipline and your kind of data. However, names should always reflect the kind of data (raw data, cleaned data, analytical data) and the data format (work file, result file etc.).
The file name should always include the date of storage (follow the YYYYMMDD format), and appear at the beginning or end of the file name to ease sorting. Do not use special characters, umlauts or spaces – use underscores instead. The names should always be uniform, clear and meaningful.
Examples for naming data files (see also: HU Berlin: Structure files ):
- \ [sediment] \ [sample] \ [instrument] \ [YYYYMMDD].dat
- \ [experiment] \ [reagent]\[instrument]\ [YYYYMMDD].csv
- \ [experiment] \ [experiment_set-up]\ [test_subject]\ [YYYYMMDD].sav
- \ [observation] \ [location] \ [YYYYMMDD].mp4
- \ [interview_partner] \ [interviewer] \ [YYYYMMDD].mp3
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The file names listed are chosen according to the “snake_case” style of writing, which uses underscores in order to replace spaces. Usually, the letters following the underscores are lower case letters, but it is also possible to use capitals. |
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Apart from snake case, there are multiple other possibilities for naming files. One of the most common ones is called “camelCase”. An example for camelCase would be: “SedimentSampleInstrumentYYYYMMDD.dat”. Each word begins with an uppercase letter – there is no underscore, as in snake_case, or any other special character. A disadvantage of this naming convention is e.g. versioning (see next paragraph). Version 1.0.0 would be recognizable as 1_0_0 using snake_case, and as 100 using camelCase. No matter which convention you choose, you should always make sure to use the same one throughout your entire research project. |
You can specify the file version in the file name in order to easily identify changes to your data. A well-known concept of versioning based on the DDI (Data Documentation Initiative) standard is: Major.Minor.Revision.
Starting from version
“1.0.0”
the following is changed:
1. the first position, if cases, variables, waves or samples are added or deleted
2. the second position, if data are corrected in a way that affects the analysis of your data
3. the third position, if there are minor revisions only that are of no consequence to interpreting your data
Versioning can also be supported by using appropriate software ( Free Version Control Software , e.g. Git).
Which file formats should I choose?
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Especially considering long-term storage and use of data, choosing the right file format is important. Usually, some characteristics are favored in file formats: they should not be encrypted, packed, proprietary/patented. Accordingly, open, documented standards are preferred. Recommendations on which file formats you should prefer can be found at RADAR , HU Berlin , UK Data Service or the Library of Congress . |
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| Data Format | Recommended | Less Suitable | Unsuitable |
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| Audio, Sound | *. flac / *. wav | *. mp3 | |
| Computer-aided Design (CAD) | *. dwg / *. dxf / *. x3d / *. x3db / *. x3dv | ||
| Databases | *. sql / *. xml | *. accdb | *. mdb |
| Raster Graphics & Images | *. dng / *. jp2 (lossless compression) / *. jpg2 ( lossless compression ) / *. png / *. tif (uncompressed) | *. bmp / *. gif / *. jp2 (lossy compression) / *. jpeg / *. jpg / *. jpg2 (lossy compression) / *. tif (compressed) | *. psd |
| Raw Data and Workspace | *. cdf (NetCDF) / *. h5 / *. hdf5 / *. he5 / *. mat (from version 7.3) / *. nc (NetCDF) | *. mat (binary) / *. rdata | |
| Spreadsheets and Tables | *. csv / *. tsv / *. tab | *. odc / *. odf / *. odg / *. odm / *. odt / *. xlsx | *. xls / *. xlsb |
| Statistical Data | *. por | *. sav (IBM®SPSS) | |
| Text | *. txt / *. pdf (PDF/A) / *. rtf / *. tex / *. xml | *. docx / *. odf / *. pdf | . doc |
| Vector Graphic | *. ait / *. cdr / *. eps / *. indd / *. psd | ||
| Video 1 | *. mkv | *. avi / *. mp4 / *. mpeg / *. mpg |
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Besides the file format (or container format), the codec used and the compression type also play an important role.
Where do I store my data during my work process?
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It is of the utmost importance to back up your data regularly in case of technical or human errors. It is the responsibility of the researcher to secure data. The Hochschulrechenzentrum (HRZ) (= university computer center) offers several possibilities for data storage: |
- Cloud Storage: JLU-Box (Ger):
| The JLUBox offers 100 GB of free cloud storage for all employees (g-identification) of Giessen University. You can share and synchronize data and work together on documents using the JLUBox. Moreover, you can provide data for students and external users, and work on documents collaboratively. |
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- Network Drives: winfile & data1 (Ger)
The HRZ offers two kinds of storing data on network drives:
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- Backup Service: IBM Spectrum Protect ISP (Ger)
| The HRZ offers regular and automated backups of servers via Tivoli Storage Manager. You have to Apply for Access to this system. The target group of this service is IT system administrators or other technically knowledgeable persons. |
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In case you need more data storage for lager research projects, please contact the HRZ ( e-mail ) at an early stage.
What should I consider when backing up my data?
Good research data management is also characterized by the fact that you, as a researcher, are prepared as best as possible for a possible data loss. Therefore, you should already create a backup plan at the beginning of your research project, which should ideally also include regular backup routines. The following questions should be answered in a backup plan:
- Which backup tool do you use?
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Which data should be backed up?
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Where do you backup your data?
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How often do you backup your data?
You should also follow the so-called 3-2-1 backup rule (s. Fig. 3 ). This rule states that you should always keep at least 3 copies of your data on 2 different data devices (e.g. a USB stick and an external hard disk) as well as 1 copy on a decentralized storage location (e.g. the JLUbox or winfile ). It is important that all 3 copies are always up to date with the original file, which is why automated backup routines are best. Instructions on how to create automated backup routines using Windows Task Scheduler can be found here .
Fig. 3: 3-2-1 Backup Rule
If you are working with personal data or other legally sensitive data, keep in mind that at least backing up to a decentralized storage location involves backing up to a tape that you no longer have any control over. For example, if you back up your data to the JLUbox, there will also be backups made at the IT Service Centre (HRZ). It is then difficult for you to comply with a possible request for deletion of the data. So please encrypt such legally sensitive data before storing it at a decentralized storage location. To do this, you can either create a zip folder that you password-protect, or you can use the VeraCrypt or Rohos MiniDrive tools. ( Which restrictions by data protection laws do I have to consider? ).
Where can I archive my data on a long-term basis?
According to good scientific practice, research data should be stored for a minimum of 10 years. In order to do that, there are several discipline-specific and generalist repositories. ( How can I find a suitable repository? )
Keep in mind that uploading your data into a repository is not the same as a publishing it. For example, you can define a period of time during which a data package is not yet accessible, but the metadata is already visible. Such embargo periods can be extended by a curator. For further information on ‘embargos’ see: Embargo (Ger). If you decide to publish your data, data access and editing rights can also be regulated in contracts or licenses. ( Can I control the use of my data? / Which license should I choose? )
Always note the respective requirements of research funders and publishers and data protection regulations. ( Who can decide on whether to share or to publish data? / Which restrictions by data protection laws do I have to consider? )
Publishing and Sharing Research Data
Why should I publish my data?
There are personal as well as scientific benefits to publishing your data. Firstly, published data is citable as independent scientific work, which increases the visibility of your own research. As studies have shown, publications will be quoted more often if the underlying data has been published (see Piwowar / Vision 2013 ).
Secondly, data sharing enables you to re-use already existing data. Therefore, new types of research questions can be investigated, without having to duplicate work or adding unnecessary costs.
Is there any reason against publishing?
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There are some situations in which you should only publish your data under specific conditions or not at all. The most important precondition for a publication is that you have the necessary rights to do so. ( Who can decide on whether to share or to publish data? / Do I own the copyyright to my data? ) |
Moreover, your data could be confidential, personal data that can only be published anonymizedly or with consent of the persons affected. ( Which restrictions by data protection laws do I have to consider? )
If you decide to publish with a publisher, make sure to choose the publisher carefully and to not fall for predatory publishing. This short presentation by Werner Dees (Ger) will give you a brief overview on how to recognize predatory publishers.
Which restrictions by data protection laws do I have to consider?
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Personal data “ means any information relating to an identified or identifiable natural person (data subject) ; an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data; an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person;” (§ 46 Abs. 1 BDSG (= General Data Protection Regulation (GDPR) ). There are strict requirements for collecting, using and passing on personal data. Information that can be linked to an identified or identifiable person has to be deleted from your research data before archiving, providing and publishing it. Depending on the kind of data, there are several ways of anonymizing data. |
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If you want to process personal data, you usually need the consent of the persons affected. The aim of your research has to be clearly defined and the persons affected have to be able to estimate the consequences.
Moreover, research data such as company data can contain confidential information (protection of undisclosed know-how and trade secrets). Additionally, non-disclosure agreements might prohibit a data publication.
Who can decide on whether to share or publish data?
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Possible rights owners or co-owners to the data are researchers, employers, customers, research funders and/or (commercial) contractors. Contracts determine who has a say in sharing or publishing research data. Usually, the results of instruction-bound research are the property of the employer/funder. However, in case of private research, researchers can decide on how to use their data on their own. |
Do I own the copyright to my data?
Research objects (some research data, too) can be protected by the copyright act as creative works. This includes literary works, computer programs, musical works, pantomimes (including choreographic works), works of the fine arts (including architecture and applied arts), photographic works, cinematographic works, and scientific and technical representations.
Usually, research data lacks the necessary threshold of originality, which is why they are not creative works. Nevertheless, there are some exceptions, such as data protected by ancillary copyright, e.g. photographs, moving pictures or sound carriers.
But often research data is protected by copyright as part of a databank or by the ancillary copyright for databanks.
Research data not protected by property rights can normally be used by anyone for any purpose without permission or obligation to pay for it.
Can I control the use of my data?
If you own the copyright or ancillary copyright to your data, you can contractually stipulate several aspects of using your data, such as how to use it, who uses it, the period and purpose of use etc. Since individual case regulations by contract are very complex, there are several solutions for standardizing regulations on rights of use. E.g. Leibniz Institute for Psychology Information (ZPID) offers standardized contracts for using data that has been gained in psychological research. Another example are GESIS user contracts (access restrictions for particularly sensitive social science data). If your data is not to be subject to any specific access or use restrictions, it is advisable to use standardized licenses such as Creative Commons or Open Data Commons. ( Which license should I choose? )
Which license should I choose?
Publishing data under a specific license allows you to specify how your data can be used in detail. This creates legal certainty for both data provider and data user. Therefore, in case of no restrictions, it is important to document this waiver clearly.
Although data are usually not subject to copyright law, you should nevertheless treat them as potentially worth protecting. Therefore, there are various license models . The most popular one is Creative Commons . CC-licenses are independent of the licensed content and cover copyright, ancillary copyright and - if existent - sui generis database rights.
The Open Knowledge Foundation’s “ Open Data Commons “ license package has been specifically created for publishing data. Apart from the unconditional license (Open Data Commons Public Domain Dedication and License (PDDL)), it offers three other packages:
- Open Data Commons Attribution License (ODC BY v1.0): Condition of attribution
- Open Data Commons Open Database License (ODbL v1.0): Condition of Share-Alike
- Database Contents License (DbCL v1.0): Condition of Share-Alike, including database contents
Regardless of its legal liability, the CC-BY license best fulfills the idea of Open Access and Open Science, whereas “Share-Alike“ can lead to compatibility issues with other licenses, and the prohibition of processing can lead to restrictions on use (e.g. data mining, issues regarding long term storage). Prohibiting commercial use will complicate using commercial databases, which reduces the potential visibility of your research.
Whichever license you choose – choose wisely. An in depth analysis on legal issues can be found here: Andreas Wiebe & Lucie Guibault (2013) . Frank Waldschmidt-Dietz’ presentation (Ger) and video on Open Educational Resources (OER) will give you further information on Creative Common licenses, licensing in general and the benefits of Open Access licenses for education.
Regardless of the terms of use, of course you have to meet the rules of good scientific practice, which require citing your sources.
How can I publish my data?
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You can choose in between publishing your data in interdisciplinary or discipline-specific data repositories. Choosing a discipline-specific data repository is usually the most convenient way, since demand for your data is most likely to be created within your discipline. Additionally, discipline-specific repositories are most likely to meet your discipline’s standards and metadata schema. Furthermore, publishing your data in a discipline-specific repository will increase your research’s visibility. Repositories such as EU-sponsored Zenodo , Dryad oder Figshare are interdisciplinary repositories. You can find a comparison in between them here (Ger). Moreover, data can be published in data supplements of scientific journals. Although this way of publishing data is becoming increasingly important, additional archiving methods should be used to ensure long-term data availability. |
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How can I find a suitable repository?
This set of questions can help you decide which repository to choose:
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In order to find a suitable repository, you can use the Registry of Research Data Repositories ( re3data.org ). Re3data is a web-based directory in which repositories are made accessible. You can simply search for a suitable repository. Numerous filters allow you to narrow down your search, e.g. by subject area or data type.
What do I have to consider when uploading data into a repository?
- File format :
It is important to use the right file format. Some repositories have strict requirements on which format to use, while others only make recommendations or are open to all formats. Therefore, you should decide on the right format at an early stage of your research process ( How do I create a good data management plan? ). General information and specific links on file formats can be found here: Which file formats should I choose?
- Metadata :
Metadata has to be documented precisely in order to make your data traceable and usable. ( What are Metadata, Metadata Schemes, Controlled Vocabularies and Documentations )
- Publication :
Uploading data into a repository does not equal instant publication. There might be reasons for an embargo period or a partial publication only. Embargos are especially common in business-related academic fields. Thus, you have to consider possible reasons accounting against an immediate publication ( Is there any reason against publishing? ).
- Conditions :
Contemplate the conditions you want to publish your data under. There are different types of licensing models to choose from ( Which license should I choose? ).
What are Metadata, Metadata Schemes and Documentations?
Metadata is data of other data or resources – in this case, of research data. It describes research data in order to
- optimize data findability,
- ensure that the data can be understood by subsequent users,
- enable linking similar research data using the same standardized metadata schema.
The most basic information includes Title, Author/Main Researcher, Institution, Persistent Identifier , Location & Time, Topic, Rights, File Name, Format etc.
Metadata schemata (i.e. metadata standards) are compilations of categories describing data. There are interdisciplinary / independent as well as discipline-specific / dependent standards. Metadata schemata are to ensure that every researcher uses the same vocabulary to describe their data, and thus to guarantee the interoperability and comparability of data sets.
The table below will give you an overview on exemplary metadata standards for several disciplines. If your specific discipline is not listed, you can have a look at the Digital Curation Center's list on disciplinary metadata .
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Discipline |
Metadata Standard |
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Interdisciplinary standards |
DataCite Schema , DublinCore , MARC21 (Ger), RADAR |
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Humanities |
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Earth sciences |
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Climate science |
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Arts & cultural studies |
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Natural sciences |
CIF , CSMD , Darwin Core , EML , ICAT Schema |
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X-ray, neutron and muon research |
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Social and economic sciences |
Before starting to document your data, you should search for existing metadata schemata. This will improve the interoperability of the research data to be created with already existing data, and it will save you the effort of developing your own metadata schema. If there is no existing metadata standard to provide the description categories necessary for your research, it is still worth using a renowned, already existing subject-specific standard as a basis to build on, e.g. by including additional categories and informing those responsible for the standard so that they can extend the schema. Metadata standards are living entities that can be adapted and enriched with new categories according to the needs of researchers. Be careful not to make any changes to existing elements or attributes, so as not to jeopardize interoperability.
It is possible, usually even necessary, to use several metadata schemata. You should always use at least one subject-independent metadata schema (preferably Dublin Core) to describe your data, because this way you can cover the general description categories as mentioned in the first paragraph of this section. Fig. 4 depicts a metadata section using Dublin Core Standard. Subject-specific metadata standards, on the other hand, allow you to structure your data using descriptive strategies, which are more based on content and may differ from discipline to discipline.
Fig. 4: Example of metadata in the Dublin Core Standard
Consequently, metadata determine which information is to be presented. To get the best possible results when searching for and using data, this information has to be presented using a uniform vocabulary. In order to do so, there are several discipline-specific and interdisciplinary controlled vocabularies , like thesauri , classifications and authority controls .
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Types of controlled vocabularies |
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Unambiguous identification of persons, items or places |
The Integrated Authority File (GND) GeoNames / International Standard Name Identifier (ISNI, ISO 27729) / |
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General, interdisciplinary classification systems |
Dewey Decimal Classification (DDC) / |
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Disciplinary classification systems |
Social Sciences (Ger) |
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Disciplinary vocabularies |
Agricultural Information Management Standard (AGROVOC) Thesaurus for the Social Sciences (TheSoz) Thesaurus Technology and Management (Ger) (TEMA)
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You can find an overview on different systems with Basel Register of Thesauri, Ontologies & Classifications (BARTOC) and Taxonomy Warehouse .
A documentation is usually more than a mere metadata description. It is a more profound (subject-specific) indexing, in the context of which e.g. variables, instruments, methods etc. are described in detail and thus the origin of the data becomes apparent. Often such a description is essential to understand, verify and use the data.
The JISC Guide on Metadata and the University of Edinburgh’s interactive Mantra Course on Documentation, Metadata, Citation will offer you further introductory information on metadata.
What are Persistent Identifiers?
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A Persistent Identifier (PID) is a code uniquely identifying a digital resource. This code is a unique, unchangeable name with which a permanent link to the digital resource can be created. Because of the unique reference, the linked content becomes citable. ( How to cite research data? ) It does not matter, which kind of resource it is – might it be a research record, journal article, video or other kinds of resources. The Persistent Identifier ensures that the resource remains retrievable if, e.g. the internet address of the server changes. Therefore, Persistent Identifiers are key to long-term storage and availability of research data. There are different kinds of PIDs. An increasingly common one is the Digital Object Identifier (DOI). An example for a DOI is: 10.5282/o-bib/2018H2S14-27. Worldwide, this DOI is only assigned once. If “https://www.doi.org/” is added in front of the DOI, this will create a link to an o-bib.de article (see https://www.doi.org/10.5282/o-bib/2018H2S14-27 ). Another common identifier for online publications, apart from DOI, is Uniform Resource Name (URN). |
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Reputable publication platforms, such as Zenodo and Figshare , automatically reserve a DOI for your data set, if you publish your data. In case you publish in a different, discipline-specific repository, you should make sure that this repository also offers DOIs or another kind of PID. ( How can I publish my data? / How can I find a suitable data repository? )
Finding and Using Research Data
Where can I find research data?
Not only due to requirements and recommendations by funders, publishers and institutions to make data accessible, research data is increasingly available for reuse. To find suitable research data for your own research, you should first have a look at relevant offers originating from your own discipline. There can be institutional or specialized repositories as well as Data Journals (List) . Repositories assorted by discipline can be found here: re3data.org
Furthermore, you can do research using generic search engines . However, to their disadvantage, they often cannot depict the detailed metadata schemata of their sources adequately. Moreover, the respective metadata differ greatly to what makes them identifiable – single data, data sets or data collections.
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Three of the most common search engines are: Metadata found in repositories and databases is obtained via OAI-PMH. You can find research data by searching for document type “Dataset“. Searches metadata by using different sources, such as CLARIN or Global GBIF. Searches entity metadata, e.g. research data (Resource Type “Dataset“) registered at DataCite using DOIs. This metadata is partly also searched by the other two services. |
When reusing data, the respective rights (licenses, license agreements) are binding. They can i.a. determine who can use the data, for which purpose and for which period of time.
If you cannot or do not want to use already existing research data, of course you can collect data using reputable research practices. For example Dr. Samuel de Haas’ and Jan Thomas Schäfer’s Coffee Lecture (Ger) will give you information on how to collect data using web scraping and text mining and on how to handle big data.
How do I cite research data?
You have to cite your data correctly to meet good scientific practice and make research data usable and reusable.
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Citing external data appreciates the scientific achievement of its “author“. As with citing other publications, conventions for citing data may formally differ. However, with regard to their content, they have to be uniquely identifiable as well. The FORCE11 Data Citation Synthesis Group has developed Recommendations for Citing Data . According to these recommendations, a full data citation includes: |
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Author(s), Year, Dataset Title, Data Repository or Archive, Version, Global Persistent Identifier .
Further, possibly useful, optional additions are Edition, URI, Resource Type, Publisher, Unique Numeric Fingerprint (UNF) and Location (see Alex Ball & Monica Duke 2015: How to Cite Datasets and Link to Publications ).
F 01
- RDM F 01
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Faculty 01: Law
Law
General Information:
- Johannes, Paul C. (2017): Forschungsdatenmanagement in der Rechtswissenschaft - Eine Betrachtung von außen nach innen. (= Research data management in law – A view from the outside to the inside). In: Die Öffentliche Verwaltung (DÖV). Heft 21. 899-905. (Access) -Ger-
Repositories:
- Catalogues des données CDSP Contains Surveys, Researchers’ Databases, and Results of Political Elections.
- Data.gov U.S. Government Page; Contains Government Data Sets, Information on Accessing these and Tools.
- <intR>2Dok[§] Open Access Repository, Central Publication Platform by the Specialist Information Service for International and Interdisciplinary Legal Research. -Ger-
- OHCHR Jurisprudence Database United Nations Human Rights Office of the High Commissioner Database for Human Rights.
- United Nations Data Access to 33 Databases and more than 60 Million Entries.
Important Standards:
- Researching Human Rights Law. Online Sources and Databases by the International Justice Resource Center: Contains Standards, Databases, and General Information. ( pdf )
F 02
- RDM F 02
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Faculty 02: Economics and Business Studies
Economics and Business Studies
General Information:
- EDaWaX (European Data Watch Extended): The Project is Funded by the DFG. It deals with Managing Research Data in Economic Journals. -Ger-
- Jensen, Uwe (2012): Leitlinien zum Management von Forschungsdaten. Sozialwissenschaftliche Umfragedaten. (= Guidelines for Management of Research Data. Social Sciences Survey Data). (pdf) -Ger-
- Jeude, Kirsten (2013): Zitieren – Recherchieren – Reproduzieren : Forschungsdaten in den Wirtschaftswissenschaften. (= Citing – Researching – Reproducing: Research Data in Economics). (pdf) -Ger-
- Liebig, Stefan (2016): Offener Zugang zu Forschungsdaten vs. Datenschutz: Institutionelle und technische Lösungsmodelle in den Sozial- und Wirtschaftswissenschaften. (= Open Access to Research Data vs. Data Protection: Institutional and Technical Solution Models for Social and Economic Sciences). (pdf) -Ger-
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten: Eine Bestandsaufnahme. Kapitel 4 Sozialwissenschaften. (= Long-Term Archiving of Research Data: Assessing the Status Quo. Chapter 4 Social Sciences). Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Rat für Sozial- und Wirtschaftsdaten (= Council for Social and Economic Data) (RatSWD)/ Stefan Liebig et al. (2014): Datenschutzrechtliche Anforderungen bei der Generierung und Archivierung qualitativer Interviewdaten. (= Requirements Regarding Data Protection Laws for Generating and Archiving Qualitative Interview Data). (pdf) -Ger-
- RatSWD (2015): Archivierung und Sekundärnutzung von Daten der qualitativen Sozialforschung – Eine Stellungnahme des RatSWD (= Archiving and Secondary Usage of Data in Qualitative Social Research - A Statement by the RatSWD). (pdf) -Ger-
- RatSWD (2016): Forschungsdatenmanagement in den Sozial-, Verhaltens- und Wirtschaftswissenschaften. (= Research Data Management in Social, Behavioral and Economic Sciences). (pdf) -Ger-
- RatSWD/ Regina T. Riphahn (2016): EU-Datenschutzgrundverordnung: Vernunft siegt. (= EU General Data Protection - Sense prevails). (pdf) (Commentary on the EU General Data Protection Regulation) -Ger-
- Risch, Uwe (2016): Aufbau einer Plattform für historische Wirtschaftsinformationen. (= Creating a Platform for Historical Economic Information). (pdf) ‑Ger-
- Schumann, Natascha & Watte, Oliver (2013): Forschungsdaten in den Sozialwissenschaften. (= Research Data in the Social Sciences.) (pdf) -Ger-
- Sektionen für Biografieforschung und für Methoden der Qualitativen Sozialforschung der DGS (2014): Resolution zur Archivierung und Sekundärnutzung von Daten. (= German Sociological Association: Department for Biographical Research and Methods of Qualitative Social Research: Resolution for Archiving and Secondary Usage of Data). (pdf) -Ger-
- Toepfer, Ralf (2016): SoWiDataNet – Umgang mit Forschungsdaten in den Wirtschaftswissenschaften und potentielle Aktionsfelder für wissenschaftliche Bibliotheken. (= SoWiDataNet - Handling Research Data in Economics and Potential Fields of Action for Academic Libraries). (pdf) -Ger-
- Vlaeminck, Sven (2016): Der Aufbau eines publikationsbezogenen Forschungsdatenarchivs für die Wirtschaftswissenschaften. (= Creating a Publication-Related Research Data Archive for Economic Sciences). (pdf) -Ger-
- Vlaeminck, Sven: Research Data Management in Economic Journals. (Access)
- ZBW (=Leibniz Information Center for Economics): Auffinden, Zitieren, Dokumentieren: Forschungsdaten in den Sozial- und Wirtschaftswissenschaften. V.2. (= Finding, Citing, Documenting: Research Data in the Social and Economic Sciences. V.2.). (pdf) -Ger-
- ZBW : Auffinden-Zitieren-Dokumentieren: Wie dokumentiere ich meine Daten und stelle sie zur Verfügung? (= Finding, Citing, Documenting: How to document and provide Data?).
- ZBW: Praktische Hinweise zum Forschungsdatenmanagement -forschung. einfach. teilen. weil teilen Wirtschaftswissen schafft. (= Practical Advice for Research Data Management – Research. Simply. Sharing. Because Sharing creates Economic Knowledge). (pdf) -Ger-
- Zenk-Möltgen, Wolfgang & Linne, Monika (2014): Metadata Schemes for datorium - Data Sharing Repository. (pdf)
Repositories:
- da | ra Registration Agency for Research Data in Social Sciences and Economic in Germany
- datorium Service for Social Sciences and Economics for Independent Documentation, Protection and Publication of Research Data. Supports the Use of Differentiated Access Conditions.
- SowiDataNet The Project aims to build a Network of Research Data for the Self-Contained Archiving and Distribution of Research Data in Economics and the Social Sciences.
- ZBW Journal Data Archive Editors can store and provide Data Sets and other Materials on Empirical Articles to support the Traceability and Replicability of Published Research Data.
Important Standards:
- DDI-Specifications:
- DDI-Codebook (DD-C 2.5) Documentation of Simple Surveys.
- DDI-Lifecycle (DD-L 3.2) Extension of DD-C to document the Entire Lifecycle of Research Data.
- ELSST (European Language Social Science Thesaurus): Multilingual Thesaurus for Political Science, Sociology, Economics, Educational Research, Educational Science, Law, Population Research, Health Care and Labor Market and Employment Research. Also included are many Terms from the Humanities (e.g. from Literary Studies and Philosophy) and from the Scientific-Technical Field (e.g. from Biology and Engineering Sciences), as well as Empirical Social Science Methodological Terms. Used by CESSDA and GESIS. -Ger-
- ICC/ESOMAR (European Society for Opinion and Market Research) (2007): International Codex for Market and Social Research. (pdf) -Ger-
- Quality Standard of Market Research ISO 20252:2012
- SDMX (Statistical Data and Metadata eXchange): Standard for Statistical Aggregate Data.
Controlled Vocabulary/Thesauri:
- STW Standard Thesaurus for Economic Sciences
F 03
- RDM F 03
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Faculty 03: Social Sciences and Cultural Studies
Social Sciences
General Information:
- Jensen, Uwe (2012): Leitlinien zum Management von Forschungsdaten. Sozialwissenschaftliche Umfragedaten. (= Guidelines for Management of Research Data. Social Sciences Survey Data). (pdf) -Ger-
- Liebig, Stefan (2016): Offener Zugang zu Forschungsdaten vs. Datenschutz: Institutionelle und technische Lösungsmodelle in den Sozial- und Wirtschaftswissenschaften. (= Open Access to Research Data vs. Data Protection: Institutional and Technical Solution Models for Social and Economic Sciences). (pdf) -Ger-
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten: Eine Bestandsaufnahme. Kapitel 4 Sozialwissenschaften. (= Long-Term Archiving of Research Data: Assessing the Status Quo. Chapter 4 Social Sciences). Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Rat für Sozial- und Wirtschaftsdaten (= Council for Social and Economic Data) (RatSWD)/ Stefan Liebig et al. (2014): Datenschutzrechtliche Anforderungen bei der Generierung und Archivierung qualitativer Interviewdaten. (= Requirements Regarding Data Protection Laws for Generating and Archiving Qualitative Interview Data). (pdf) -Ger-
- RatSWD (2015): Archivierung und Sekundärnutzung von Daten der qualitativen Sozialforschung – Eine Stellungnahme des RatSWD (= Archiving and Secondary Usage of Data in Qualitative Social Research - A Statement by the RatSWD). (pdf) -Ger-
- RatSWD (2016): Forschungsdatenmanagement in den Sozial-, Verhaltens- und Wirtschaftswissenschaften. (= Research Data Management in Social, Behavioral and Economic Sciences). (pdf) -Ger-
- RatSWD/ Regina T. Riphahn (2016): EU-Datenschutzgrundverordnung: Vernunft siegt. (= EU General Data Protection - Sense prevails). (pdf) (Commentary on the EU General Data Protection Regulation) -Ger-
- Schumann, Natascha & Watte, Oliver (2013): Forschungsdaten in den Sozialwissenschaften. (= Research Data in the Social Sciences.) (pdf) -Ger-
- Sektionen für Biografieforschung und für Methoden der Qualitativen Sozialforschung der DGS (2014) (= German Sociological Association: Department for Biographical Research and Methods of Qualitative Social Research): Resolution zur Archivierung und Sekundärnutzung von Daten. (= Resolution for Archiving and Secondary Usage of Data). (pdf) -Ger-
- Wetcher-Hendricks, Debra (2014): Analyzing Quantitative Data: An Introduction for Social Researchers . Hoboken: Wiley. (Ebook) , (Alternative Access via via library system )
- ZBW (=Leibniz Information Center for Economics): Auffinden, Zitieren, Dokumentieren: Forschungsdaten in den Sozial- und Wirtschaftswissenschaften. V.2. (= Finding, Citing, Documenting: Research Data in the Social and Economic Sciences. V.2.). (pdf) -Ger-
- ZBW : Auffinden-Zitieren-Dokumentieren: Wie dokumentiere ich meine Daten und stelle sie zur Verfügung? (= Finding, Citing, Documenting: How to document and provide Data?).
- Zenk-Möltgen, Wolfgang & Linne, Monika (2014): Metadata Schemes for datorium - Data Sharing Repository. (pdf)
Repositories:
- datorium Service for Social Sciences and Economics for Independent Documentation, Protection and Publication of Research Data. Supports the Use of Differentiated Access Conditions.
Important Standards:
- DDI-Specifications:
- DDI-Codebook (DD-C 2.5) Documentation of Simple Surveys.
- DDI-Lifecycle (DD-L 3.2) Extension of DD-C to document the Entire Lifecycle of Research Data.
- ELSST (European Language Social Science Thesaurus): Multilingual Thesaurus for Political Science, Sociology, Economics, Educational Research, Educational Science, Law, Population Research, Health Care and Labor Market and Employment Research. Also included are many Terms from the Humanities (e.g. from Literary Studies and Philosophy) and from the Scientific-Technical Field (e.g. from Biology and Engineering Sciences), as well as Empirical Social Science Methodological Terms. Used by CESSDA and GESIS. -Ger-
- Quality Standard of Market Research ISO 20252:2012
- SDMX (Statistical Data and Metadata eXchange): Standard for Statistical Aggregate Data
Education Science
General Information:
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kap. 6: Pädagogik und Erziehungswissenschaft. (= Long-Term Archiving of Research Data. Assessing the Status Quo. Chapter 6: Pedagogy and Education Science). Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Stanat, Petra (2012): Bereitstellung und Nutzung quantitativer Forschungsdaten in der Bildungsforschung: Memorandum des Fachkollegiums „Erziehungswissenschaft“ der DFG . (= Providing and Using Quantitative Research Data in Education Science: Memorandum of the Members of the DFG's Council for Education). (pdf)
Repositories:
- Portal Research Data - Education Overview on Studies of Empirical Educational Research, Reusing Data, and Research Data Management. -Ger-
Musicology
General Information:
- Abdallah, Samer et al. (2017): The Digital Music Lab: A Big Data Infrastructure for Digital Musicology. In: Journal on Computing and Cultural Heritage 10.1. Article 2 (January 2017) DOI: https://doi.org/10.1145/2983918
- Albrecht-Hohmaier, Martin (2018): Vernetzung und Langzeitarchivierung von digitalen musikwissenschaftlichen Forschungsdaten im WWW. (= Networking and Long-Term Archiving of Digital Musicological Research Data on the WWW). In: Symposiumsbericht »Stand und Perspektiven musikwissenschaftlicher Digital Humanities-Projekte« Beitragsarchiv des Internationalen Kongresses der Gesellschaft für Musikforschung. Berthold Over und Torsten Roeder (Hg.). Mainz: Schott Campus. URN: urn:nbn:de:101:1-2018091710375228835197. (Access) -Ger-
- Balke, Stefan (2018): Multimedia Processing Techniques for Retrieving, Extracting, and Accessing Musical Content . Nürnberg: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Diss. URN: urn:nbn:de:bvb:29-opus4-96358. (Access)
- Besson, Vincent et al. (2016): A Methodology for Quality Assessment in Collaborative Score Libraries. Proceedings of the 17th International Society for Music Information Retrieval Conference, Aug 2016, New York City, United States. (Access)
- Deutsche Gesellschaft für Musikpsychologie (= German Society for Music Psychology) (2018): Konkretisierung der DFG Richtlinien zum Umgang mit Forschungsdaten. Empfehlungen der Deutschen Gesellschaft für Musikpsychologie e.V. (= Specifying the German Research Foundation’s Guidelines for Handling Research Data. Recommendations of the German Society for Music). (pdf) -Ger-
- Musiconnn Specialist Information Service for Musicology.
- Memorandum der Gesellschaft für Musikforschung zur Schaffung nationaler Forschungsdateninfrastrukturen (NFDI) (2017). (= Society for Music Research’s Memorandum on Creating National Research Data Infrastructures). (Access) -Ger-
- Neovesky, Anna (2018): Und was machen wir nun mit den Daten? Nutzungsszenarien und deren Voraussetzungen am Beispiel von Akademievorhaben. (= What shall We do with the Data? Usage Scenarios and their Prerequisites Using the Example of Academy Projects). Zenodo. DOI: https://doi.org/10.5281/zenodo.1175632 -Ger-
- Oevel, Gudrun (2016): Der Ton macht die Musik. Digitalisierung von Forschungsprozessen nicht nur in der Musikwisssenschaft (=It’s not What You say but how You say It. Digitalization of Research Processes not only in Musicology). In: „ Ei, dem alten Herrn zoll‘ ich Achtung gern“: Festschrift für Joachim Veit zum 60. Geburtstag . München: Allitera Verlag. S. 587-597. DOI: http://dx.doi.org/10.25366/2018.38 -Ger-
- Sydney Conservatorium of Music (2015): Research Data Management Provisions. (pdf)
- Workshop zu Forschungsdaten in der Musikwissenschaft / audio-visuelle Kulturgüter (2018): Zusammenfassendes Protokoll. (= Workshop on Research Data in Musicology / Audio-Visual Cultural Artifacts: Summarizing Protocol). (pdf) -Ger-
- Wünsche, Stephan (2015): Forschungsdaten in musikwissenschaftlichen und musikpädagogischen Dissertationen: Eine Stichprobe anhand der im Jahr 2015 in Deutschland angenommenen Arbeiten (= Research Data in Musicological and Music Medagogical Dissertations. A Sample based on the Papers Accepted in Germany in 2015). Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft Heft 433. Berlin: Institut für Bibliotheks- und Informationswissenschaften der Humboldt-Universität zu Berlin. DOI: https://doi.org/10.18452/19457 -Ger-
Repositories:
- DARIAH-DE Repository
- Digital Collections Goethe University Frankfurt am Main
- DIAMM Digital Image Archive of Medieval Music
- ECHO Cultural Heritage Online
- Europeana Collections
- GloPAD Global Performing Arts Database
- Loeb Music Library
- Maison méditerranéenne des sciences de l’homme, Phonothèque: Phonothèque MMSH
- Medienarchiv der Künste
- Mutopia Project Collection of Free Sheet Music
- PARADISEC Pacific and Regional Archive for Digital Sources in Endangered Cultures
- Phonogrammarchiv
- Research Catalogue Society for Artistic Research
- Sound and Vision
- SLUB Collections
- Studyforrest
- USC San Diego: Library Digital Collections
- Central Register of Digitalised Prints -Ger-
Pedagogy
General Information:
- Cummings, Rebekah & Stephenson, Libbie (2012): Data Management for Education Research. (Access)
- Figlio, David N. & Salvanes, Kjell G. & Karbownik, Krysztof (2015): Education research and administrative data . Cambridge, Mass.: National Bureau of Economic Research. DOI: http://dx.doi.org/10.3386/w21592
- Stanat, Petra (2015): Bereitstellung und Nutzung quantitativer Forschungsdaten in der Bildungsforschung: Memorandum des Fachkollegiums Erziehungswissenschaft der DFG. (= Providing and Using Quantitative Research Data in Education Science: Memorandum of the Members of the DFG's Council for Education Science). In: Erziehungswissenschaft 26.50. 75-89. (pdf) -Ger-
General Information - Printed Media:
- Johnson, Burke & Larry Christensen (2012): Educational research: Quantitative, qualitative, and mixed approaches . LA u.a.: SAGE.
- Peez, Georg (Hg.) (2009): Kunstpädagogik und Biografie: 52 Kunstlehrerinnen und Kunstlehrer erzählen aus ihrem Leben. Professionsforschung mittels autobiografisch-marrativer Interviews. (= Art Education and Biography: 52 Art Teachers are talking about their Lives. Professional Research using Autobiographic-Narrative Interviews). München: kopaed.
Repositories:
- Child Care & Early Education Research Connections
- Education Research Portal
- ICPSR Inter-University Consortium for Political and Social Research
- IQB Berlin Research Data Center at the Institute for Educational Quality Improvement
- National Center for Educational Statistics
- NCAA Student-Athlete Experiences Data Archive
- NEPS National Educational Panel Study
Political Science
General Information - Printed Media:
- Kellstedt, Paul (Hg.) (2013): Political Science Research and Methods. Cambridge UP. ISSN: 2049-8470 (Print), 2049-8489 (Online).
Repositories:
- AmericasBarometers
- ANES American National Election Studies
- CESSDA Consortium of European Social Science Data Archives
- CPDS Comparative Political Data Set
- CSES Comparative Study of Electoral Systems
- datorium
- ICPSR Inter-University Consortium for Political and Social Research
- POLLUX Information Service for Political Science, enables you to search specifically for Research Data.
- PolMine Project
- The Correlates of War Project
- The Quality of Government Institute
Sociology
General Information:
- Crossmann, Ahsley (2018): Data Source for Sociological Research . (Access)
- Friedhoff, Stefan et al. (2013): Social Research Data. Documentation, Management, and Technical Implementation at SFB 882 . DFG Research Center (SFB) 882. (Access)
- Perry, Anja & Recker, Jonas (2018): Sozialwissenschaftliche Forschungsdaten langfristig sichern und zugänglich machen: Herausforderungen und Lösungsansätze. (= Long-Term Storing of Social Sciences Research Data and Making them Accessible: Challenges and Solution Approaches). DOI: https://doi.org/10.5282/o-bib/2018H2S106-122 -Ger-
- Postitionspaper der Akademie für Soziologie (2019): Wissenschaftliche Daten sind kein Privateigentum einzelner Forschender, sondern ein kollektives Gut. Die Bereitstellung von Forschungsdaten zur Nachnutzung und Replikation muss auch in der Soziologie die Norm sein. (= Positionpaper by the Academy for Sociology: Scientific Data is not the Private Property of Individual Researchers but a Collective Good. Provision of Research Data for Reusage and Replication must be the Norm in Sociology as well). (pdf) -Ger-
Repositories:
F 04
- RDM F 04
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Faculty 04: History and Cultural Studies
History
General Information:
- Andorfer, Peter (2015): Forschen und Forschungsdaten in den Geisteswissenschaften: Zwischenbericht einer Interviewreihe. (= Research and Research Data in the Humanities: Interim Report of an Interview Series). (pdf) -Ger-
- FORGE (2015): Forschungsdaten in den Geisteswissenschaften (= Research Data in the Humanities).
- FORGE (2016): Forschungsdaten in den Geisteswissenschaften – Jenseits der Daten. (= Research Data in the Humanities - Beyond Data)
- Historikerverband, AG Digitale Geisteswissenschaften (= German Historical Association, Project Group Digital Humanities): Digitale Fachinformationen und Kommunikation. (= Digital Information and Communication). (Access) -Ger-
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kap. 7: Geisteswissenschaften (= Long-Term Archiving of Research Data. Assessing the Status Quo. Chapter 7: Humanities). Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Sahle, Patrick & Kronenwett, Simone (2013): Jenseits der Daten: Überlegungen zu Datenzentren für die Geisteswissenschaften am Beispiel des Kölner „Data Center for the Humanities“. (= Beyond Data: Deliberations on Data Centers for the Humanities as shown by using the Example of the Cologne “Data Center for the Humanites”). ( Access) -Ger-
- Stäcker, Thomas (2015): Noch einmal: Was sind geisteswissenschaftliche Forschungsdaten? (= Once again: What are Research Data in the Humanities?). (Access) -Ger-
Repositories:
- CLARIN-D For Subdisciplines of the Humanities and Social Sciences.
- DARIAH-DE Interconnecting Several Disciplines from the Humanities and Cultural Studies, Supporting the Exchange of Resources, Methods, Data and Experience through Building Up a Digital Research Infrastructure, Searching for Research data and a Sustainable Storage of Research Data. -Ger-
- FuD Virtual Research Environment for the Humanities. -Ger-
- Humanities Data Centre -Ger-
- IANUS Data Portal -Ger-
- Historisches Datenzentrum Sachsen-Anhalt Bereitstellung, Aufbereitung und Auswertung historischer Quellen, Anwendung und Weiterentwicklung von Methoden im Rahmen von eHumanties und Historischer Statistik (= Provision, Processing and Evaluation of Historical Sources, Application and further Development of Methods in the Context of eHumanties and Historical Statistics). -Ger-
Classical Studies
General Information:
- IANUS Research Data Center Archaeology and Classical Studies. -Ger-
- Handout on Digital Research Data in Classical Studies. (pdf) -Ger-
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kapitel 8: Altertumswissenschaften. (= Long-Term Archiving of Research Data. The Status Quo. Chapter 8: Classical Studies) . Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Propylaeum: Specialized Information Service Classics: Research data. (Access)
- Schäfer, Felix (2015): Ein längeres Leben für Deine Daten! Altertumswissenschaften in a Digital Age: Egyptology, Papyrology and beyond. (= A longer life for your data! Classical studies in a digital age: Egyptology, Papyrology and beyond). Presentation. (Access) -Ger-
- Schäfer, Felix (2015): IANUS – ein fachspezifischer Ansatz zur Archivierung von Forschungsdaten. (= IANUS – A Discipline-Specific Approach to Archiving Research Data). Presentation. (pdf) -Ger-
- Schäfer, Felix et al. (2014): Forschungsrohdaten für die Altertumswissenschaften – eine kurze Bilanz der aktuellen Situation von Open Data in Deutschland. (= Raw Research Data for Classical Studies – a Brief Look at the Status Quo of Open Data in Germany). In: Archäologische Informationen 38. DOI: https://doi.org/10.11588/ai.2015.1.26156 -Ger-
- Schäfer, Felix & Heinrich, Maurice (2014): Archivierung von digitalen Forschungsdaten in den Altertumswissenschaften. (= Archiving Digital Research Data in the Classical Studies) In: Jörg Filthaut (Hg.). Von der Übernahme zur Benutzung. Aktuelle Entwicklungen in der digitalen Archivierung, 18. Tagung des Arbeitskreises "Archivierung von Unterlagen aus digitalen Systemen“ (Weimar 2014). S. 11-20. (Access) -Ger-
Repositories:
- Ancient Columns
- ARACHNE IDAI.objects -Ger-
- ADS Archaeology Data Service
- Babylonian Diaries
- The Book of Caverns in Theban Tomb 33
- BowPed TRPS Data
- DART Data Detection of Archaeological Residues using Remote-Sensing Techniques
- Digital Pantheon
- IANUS Data Portal -Ger-
- ICG Inscriptiones Christianae Graecae
- Johanna Mestorf Academy Research Data Exchange Platform
- Maya Image Archive
- The Neolithic in the Nile Delta
- Open Context
- Rock Paintings in Indonesia
- Roman Villa of Capo di Sorrento
- tDAR The Digital Archaeological Record
- UvaDoc University of Valladolid Documentory Repository - Fonda Antiguo
- WBD Wolfenbüttel Digital Library -Ger-
Catholic Theology / Protestant Theology
General Information:
- Cartledge, Mark J. (2016): Public Theology and Empirical Research: Developing an Agenda. In: International Journal of Public Theology 10.2. DOI: https://doi-org.ezproxy.uni-giessen.de/10.1163/15697320-12341440
- Kørup, Alex Kappel et al. (2017): The International NERSH Data Pool: A Methodological Description of a Data Pool of Religious and Spiritual Values of Health Professionals from six Continents. In: Religions 8.2 S. 24. DOI: 10.3390/rel8020024
- University of Amsterdam. Data Management in Religious Studies. (Access)
Repositories:
- ARDA Association of Religion Data Archives
- Codex Sinaiticus
- Inscriptiones Christianae Graecae
- RELMIN Database: Legal Status of Religious Minorities in the European Medieval World, 5th-15th Century
- World Christian Database
Philosophy
General Information:
- Mathiak, Brigitte & Kronenwett, Simone (2017): Forschungsdaten an der Philosophischen Fakultät der Universität zu Köln. (= Research Data at the Faculty of Philosophy of Cologne University). Presentation. (Access) -Ger-
- University of Amsterdam. Data Management in Philosophy. (Access)
Repositories:
- Data and Service Center for the Humanities
- Digital Averroes Research Environment
- Digital Collections (Goethe Universität Frankfurt am Main) -Ger-
- ECHO Cultural Heritage Online
Islamic Studies
General Information:
- Awadallah, Rawia & Al Agha, Iyad (2017): Assessing Current Open Access and Research Data Management Practices and Services in Palestinian HEIs. Zenodo. DOI: 10.5281/zenodo.801735 .
- Dalhat, Yusuf (2015): Introduction to Research Methodology in Islamic Studies. Journal of Islamic Studies and Culture 3.2. 147-152. DOI: 10.15640/jisc.v3n2a15
- Hanapi, Mohd Shukri & Wan Mohd Kairul Firdaus Wan Khairuldin (2017). Applying the Thematic Hadith Method in Research related to Islam. International Journal of Academic Research in Business and Social Sciences 7.12. 576-586. DOI: 10.6007/IJARBSS/v7-i12/3639
- Wan Mokhtar, Wan Khairul Aiman. (2017). Concept Al-Hadīth Al-Mawḍū'iy as a Method of Collecting and Analysing research's Data. International Journal of Academic Research in Business and Social Sciences . 7.2. 536-542. (pdf)
Repositories:
- Al-Islam
- Altafsir
- ARDA Association of Religion Data Archives
- ECHO Cultural Heritage Online
- European History Online
- ISAM Center for Islamic Studies in Istanbul
- Islamic Philosophy Online
- MENAdoc
- MENALIB The Middle East Virtual Library
- RELMIN
Turkish Studies
General Information:
- Aydinoglu, Arsev Umur & Dogan, Guleda & Taskin, Zehra (2017): Research Data Management in Turkey: Perceptions and Practices. In: Library Hi Tech 35.2. 271-289. DOI: https://doi.org/10.1108/LHT-11-2016-0134
- Stellungnahme der Gesellschaft für Turkologie, Osmanistik und Türkeiforschung e.V. (2018) (= Statement by the Society for Turkish Studies, Ottoman Studies and Research in Turkey) (pdf) -Ger-
- Tonta, Yasar (2008): Open Access and Institutional Repositories: The Turkish Landscape. In: Turkish Libraries in Transition: New Opportunities and Challenges . Turkish Librarian’s Association. 27-47. (Access)
- Ünal, Yurdagül & Kurbanoğlu, Serap. Research Data Management Practices of Academic Researchers in Turkey, Text, October 25, 2017; (pdf) (accessed May 18, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu ; Crediting UNT College of Information.
- The University of Michigan’s Guide to Library Resources in Turkish, Ottoman, Turkish Languages and Turkish Studies. (Access)
Repositories:
- Cumhuriyet Dönemi Makaleler Bibliografyasi
- Historians of the Ottoman Empire
- MENAdoc Digital Collections
- MENALIB The Middle East Virtual Library
- The Online Bibliography of Ottoman-Turkish Literature
- TS Corpus
- The Turkology Annual Online
Art History
General Information:
- Arbeitskreis Digitale Kunstgeschichte (= Workgroup Digital Art History) (Access) ‑Ger-
- arthistoricum.net: Research data
- Blaser-Meier, Susanna (2019): Embedded Librarianship und Forschungsdatenmanagement in den Geisteswissenschaften: Fallstudien aus der Kunstgeschichte (=Embedded Librarianship and Research Data Management in the Humanities: Case Studies from Art History) Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft 434. Berlin: Institut für Bibliotheks- und Informationswissenschaft der Humboldt-Universität zu Berlin. (Access via library system JLU ) -Ger-
- Drucker, Johanna et al. (2015): Digital Art History: The American Scene. In: Perspective. Actualité en histoire de l’art 2. DOI: 10.4000/perspective.6021
- Klinke, Harald & Surkemper, Liska (2016): Big Image Data as New Research Opportunity in Art History. (pdf)
- Kuroczyński, Piotr &Bell, Peter & Dieckmann, Lisa (Hg.) (2018): Computing Art Reader: Einführung in die digitale Kunstgeschichte (= Introduction to digital art history) Computing Art and Architecture, Bd. 1. Heidelberg: arthistoricum.net. DOI: https://doi.org/10.11588/arthistoricum.413 -Ger-
- Open Access: Art History
- Promey, Sally M. & Stewart, Miriam (1997): Digital Art History: A New Field for Collaboration. In: American Art 11.2. 36–41. (Access) (Alternative access via library system JLU )
- Simukovic, Elena et al. (2014): Was sind Ihre Forschungsdaten? Interviews mit Wissenschaftlern der Humboldt-Universität zu Berlin. (= What is your Research Data? Interviews with Scientists of Humboldt University Berlin). Report, Version 1.0. URN: urn:nbn:de:kobv:11-100224755 -Ger-
- University of Amsterdam: Data Management in History of Art. (Access)
- Warnke, Martin (2003): Daten und Metadaten – Online Ressourcen für die Bildwissenschaft. (= Data and Metadata – Online Resources for Pictorial Science). In: Zeitenblicke 2.1. (Access) -Ger-
- Züricher Erklärung zur digitalen Kunstgeschichte (2014) (= Zurich Declaration on Digital Art History). (pdf) -Ger-
Repositories:
- Arachne IDAI.objects -Ger-
- arthistoricum.net
- Artstor
- cranach.net -Ger-
- Deutsches Dokumentationszentrum für Kunstgeschichte: Bildarchiv Marburg (= German Documentation Center for Art History: Image Archive Marburg). ‑Ger-
- Digital Pantheon
- ECHO Cultural Heritage Online
- European History Online
- Prometheus Image Archiv
- sandrart.net
- SLUB Dresden Collections
- Wand- und Deckenmalerei in Lübecker Häusern 1300 bis 1800 (= Mural and Ceiling Paintings in Lübeck Houses from 1300 to 1800). -Ger-
F 05
- RDM F 05
-
Faculty 05: Language, Literature, Culture
German Studies
General Information:
- Bankhardt, Christina (2017): The Reusability of Linguistic Research Data from a Legal and Ethical Point of View . Dissertation. (Access) -Ger-
- Daudrich, Anna (2018): Handling Digital Research Data in the Humanities and Social Sciences. (Access) -Ger-
- Dipper, Stefanie et al. (2006): Sustainability of Linguistic Resources. In: Proceedings of the LREC 2006 Workshop on Merging and Layering Linguistic Information, Genoa, Italy S. 48-54. (Access)
- Frankhauser, Peter et al. (2013): Forschungsdatenmanagement in den Geisteswissenschaften am Beispiel der germanistischen Linguistik. (= Research Data Management in the Humanities as Shown by Using the Example of German Linguistics). (pdf) -Ger-
- Lobin, Henning & Schneider, Roman & Witt, Andreas (Hg.) (2018): Digitale Infrastrukturen für die germanistische Forschung (= Digital Infrastructure for Research in German Studies). Berlin und Boston: De Gruyter. (Access via library system JLU )
- Rehm, Georg et al. (2010): Sustainability of Linguistic Resources Revisited. In: Proceedings of the International Symposium on XML for the Long Haul: Issues in the Long-term Preservation of XML 6. DOI: https://doi.org/10.4242/BalisageVol6.Witt01
- Renner-Westermann, Heike (2018): Fachinformationsdienst Linguistik zwischen Innovation und Tradition: Forschungsdaten in der Linguistik. (= Spezialised Information Center for Linguistics in between Innovation and Tradition: Research Data in Linguistics). In: Zeitschrift für Bibliothekswesen und Bibliographie 65.2-3 90-93. DOI: http://dx.doi.org/10.3196/1864295018652368 -Ger-
Repositories:
- Bavrian Archive for Speech Signals
- D-PLACE Database of Places, Language, Culture and Environment
- Database for Spoken German
- Deutsches Textarchiv -Ger-
- GerManC A Representative Historical Corpus of German 1650-1800
- HZSK The Hamburg Center for Linguistic Corpora
- LAUDATIO-Repository Long-Term Access and Usage of Deeply Annotated Information
- SLUB Saxon State and University Library Dresden
- TextGrid Repository
- Virtuelles Skriptorium St. Matthias Digitalization of the Medieval Library of Trier Abbey St. Matthias -Ger-
- Zentrum für germanistische Forschungsprimärdaten (2013 - 2017) -Ger-
Romance Studies
General Information:
- Arbeitsgruppe Digitale Romanistik des DRV. (= Workgroup Digital Romance Studies of the German Romance Association). (Access) ‑Ger-
- Deutscher Romanistenverband (= German Romance Association) (2017): Positionspapier: Open Access und Forschungsdaten in der Romanistik. (= Position paper: Open Access and Research Data in Romance Studies). (pdf) -Ger-
- Erben, Maria & Grüter, Doris & Rohden, Jan (2018): Forschungsdatenmanagement in der Romanistik. Aktuelle Situation und zukünftige Perspektiven. (= Research Data Management in Romance Studies. Status Quo and Future Perspectives.) Bonn. (Access) ‑Ger-
- FID Romanistik: Ergebnisse des Workshops zum Forschungsdatenmanagement in der Romanistik. (= FID Romanistic: Results form the Workshop on Research Data Management in Romance Studies). (pdf) -Ger-
- Schwerpunktthema Langzeitarchivierung von Forschungsdaten (2017): Workshop zum Forschungsdatenmanagement in der Romanistik – Bericht. (= Key Issue Long-Term Archiving of Research Data: Workshop on Research Data Management in Romance Studies. (pdf) -Ger-
Repositories:
- French Studies:
- Centre National de Ressources Textuelles et Lexicales
- CINES Preservation Centre Informatique National de l’Enseignement Supérieur
- NAKALA
- ORTOLANG Outils et Ressouces pour un Traitement Optimisé de la LANGue
- Queteleg PROGEDO Diffusion
- Italien Studies:
- Eurac Research Clarin Centre
- ILC-CNR for CLARIN-IT
- PHAIDRA Digital Collections
- UNIDATA Bicocca Data Archive ( Data since 2008 ; Data before 2008 )
- Portuguese Studies:
- Arquivo.pt Arquivo da Web Portugesa
- Spain Studies:
- Centro de Competencias CLARIN del IULFA-UPF
- DIGITAL.CSIC repositorio del Consejo Superior de Investigaciones Científicas
- Repositorio Institucional de la Universidad San Ignacio de Loyola
Slavic Studies
General Information:
- Meyer, Roland (2018): Die Slavistik in einer nachhaltigen digitalen Forschungsdateninfrastruktur. (= Slavic Studies as Part of a Durable Research Data Infrastructure). (pdf) -Ger-
- Pagenstecher, Cord: Zeitzeugen-Interviews als Forschungsdaten. (= Interviews with Contemporary Witnesses as Research). (pdf) -Ger-
Repositories:
- CLARIN-PL
- Kujawsko-Pomorska Digital Library
- Open Data Portal Russia
- RepOD
- Slavistik-Portal
- SSRC List: Partial Listing of General, Comparative, Open Access Survey Data Sets for the Eurasia Region
- University Information System RUSSIA
English Studies
General Information:
- Forkel, Robert et al. (2018): Cross-Linguistic Data Formats, Advancing Data Sharing and Re-use in Comparative Linguistics. Scientific data 5.180205. 16 Oct. DOI: 10.1038/sdata.2018.205
- Hamrin, Göran & Voß, Viola (2018): Quadcopters or Linguistic Corpora – Establishing RDM Services for Small-Scale Data Producers at Big Universities. Proceedings of the IATUL Conferences . Paper 3. (Access)
- Research Data Oxford
- Paltridge, Brian (Hg.) (2013): Continuum Companion to Research Methods in Applied Linguistics. London u.a.: Bloomsbury. DOI: https://doi.org/10.1093/elt/ccr054
- Prøitz, Tine & Mausethagen, Sølvi & Skedsmo, Guri. (2017): Investigative Modes in Research on Data Use in Education. In: Nordic Journal of Studies in Educational Policy 3. 1-14. DOI: https://doi.org/10.1080/20020317.2017.1326280
- Research Data Oxford
- Thieberger, Nicholas (Hg.) (2012): The Oxford Handbook of Linguistic Fieldwork. Oxford: OUP. (Access via library system JLU )
- University of Cambridge: Research Data Management. (Access)
Repositories:
- Australian National Corpus
- Buckeye Speech Corpus
- CHILDES Child Language Data Exchange System
- CLARIN-UK
- English Lexicon Project
- Eurac Research Clarin Centre
- The Language Archive
- Linguistic Data Consortium
- META-SHARE
- MICASE Michigan Corpus of Academic Spoken English
- OLAC Open Language Archives Community
- UCLA Phonetics Lab Archive
- UdS Fedora Commons Repository
- WALS The World Atlas of Language Structures
Applied Theatre Studies
General Information:
- Clarke, Paul & Jones, Sarah & Gray, Stephen (2013): Managing Creative Arts Research Data. Post Graduate Module (Bristol/DCC). Zenodo. DOI: http://doi.org/10.5281/zenodo.28548
- Duca, Daniela (2016): Research Data in the Creative and Performing Arts. (Access)
- Goller, Marion & Heftberger, Adelheid (2018): Die Öffnung von Forschungsdaten in den Film- und Medienwissenschaften: praktische und urheberrechtliche Herausforderungen. (= Publishing Research Data in Film and Media Studies: Practical and Copyrighted Challenges). In: Fachinformationsdienst für internationale und interdisziplinäre Rechtsforschung . DOI: https://doi.org/10.17176/20180515-233758 ‑Ger-
- Guy, Marieke & Donnelly, Martin & Molly, Laura (2013): Pinning it down: Towards a Practical Definition of ‘Research Data’ for Creative Arts Institutions. In: The International Journal of Digital Curation 8.2. 99-110. DOI: https://doi.org/10.2218/ijdc.v8i2.275
- Janßen, Melanie (2019): Comparison and Analysis of Research Data Repositories. An Exemplary Study of Handling Research Data Focusing on Video Resources. BA thesis. Potsdam: University of Applied Science Potsdam. (Access) -Ger-
- Kershaw, Baz & Nicholson, Helen (2011): Research Methods in Theatre and Performance . Edinburgh UP. (Access) (Alternative access via library system JLU )
- Montero, Gustavo Grandal (2013): Art Resources Online: Research Data Management. (Access)
Repositories:
- Brooklyn College Theatre Research Data Center
- CMU Graphics Lab Motion Capture Database
- Cultural Policy and the Arts National Data Archive
- GloPAD Global Performing Arts Database
- Medienarchiv der Künste
- Motion Capture Database HDM05
- Phonogrammarchiv
- Society for Artistic Research: Research Catalogue
- Sound and Vision
- University of the Arts London Data Repository
- VADS Visual Arts Data Service
- Wittliff Collections Texas Staate University, Digital Collections Repository
F 06
- RDM F 06
-
Faculty 06: Psychology and Sport Science
Psychology
General Information:
- Dehnhard, Ina et al. (2017): Publikation von Forschungsdaten. Vorstellung der Repositorienlandschaft in der Psychologie. (= Publishing Research Data. Presenting the Range of Psychology Repositories). Leipzig: ZPID. Presentation. DOI: http://dx.doi.org/10.23668/psycharchives.677 -Ger-
- Günther, Armin (2011): Forschungsdatenmanagement in der Psychologie. Rahmenbedingungen – Ansätze – Perspektiven. Wiesbaden: Konferenz für Sozial- und Wirtschaftsdaten. (= Research Data Management in Psychology. General Conditions - Approaches – Perspectives). Wiesbaden: Konferenz für Sozial- und Wirtschaftsdaten. 5th Presentation. (pdf) -Ger-
- Kerwer, Martin et al. (2017): Projekt DataWiz: Entwicklung eines Assistenzsystems zum Management psychologischer Forschungsdaten . (= Project DataWiz: Developing an Assisting System for Managing Psychological Research Data) In: Jonas Kratzke und Vincent Heuveline (Hg.). E-Science-Tage 2017: Forschungsdaten managen. Heidelberg: heiBOOKS. S. 170-187. DOI: 10.11588/heibooks.285.377 -Ger-
- Schönbrodt, Felix & Gollwitzer, Mario & Abele-Brehm, Andrea (2016): Data Manag ement in Psychological Science: Specification of the DFG Guidelines . (pdf)
- Weichselgartner, Erich (2017): DataWiz: Integration von Open-Science-Praktiken in den Forschungsdatenzyklus. (= DataWiz: Integrating Open-Science-Practices into the Research Data Life Cycle). In: Information – Wissenschaft & Praxis 68.2-3. 159-162. DOI: https://doi.org/10.1515/iwp-2017-0023 -Ger-
- Weichselgartner, Erich (2017): Forschungsdatenmanagement in der Psychologie: Anforderungen, Werkzeuge, Standards. (= Research Data Management in Psychology. Requirements, Tools, Standards). Trier: Universität Trier. Presentation. (pdf) -Ger-
Repositories:
- Brainlife
- Centers for Disease Control and Prevention
- CFAS Cognitive Function & Ageing Study
- CPES Collaborative Psychiatric Epidemiology Surveys 2001-2003 (ICPSR 20240)
- FORSbase
- ICPSR Inter-university Consortium for Political and Social Research
- NDACAN National Data Archive on Child Abuse and Neglect
- NTDB National Trauma Database
- OpenNeuro
- Psi Open Data
- Psychdata Data Platform especially for Psychological Research
- Socio Cognitive Processes Lab data
Sport Science
General Information:
- Abel, Meike (2015): Open Science in der Sportwissenschaft. Das Projekt Mo|Re data: Sportwissenschaftlicher Forschungsdaten zitierfähig aufbereiten. (= Open Science in Sport Science. Project Mo|Re data: Processing Sport Scientific Research Data to make them citeable). Karlsruhe: KIT. Presentation. (pdf) -Ger-
- Albrecht, Claudia et al. (2016): Handreichung Forschungsdatenmanagement in der Sportwissenschaft. (= Handout Research Data Management in Sport Science). Karlsruhe: KIT. Presentation. (pdf) -Ger-
- De Pauw, Kevin et al. (2013): Guidelines to classify Subject Groups in Sport Science Research. In: International Journal of Sports Physiology and Performance 8. 111-122. (pdf)
- Veljović, Dragoljub & Medjedovic, Bojan & Ostojic, Sergej. (2011): Current Research Problems in Sport Sciences. In: TIMS. Acta 5. 37-46. (Access)
- Victoria University Melbourne. 10 Sports Science Data Things. (Access)
- Vincent, Jenny & Stergiou, Pro & Katz, Larry (2009): The Role of Databases in Sport Science: Current Practice and Future Potential. In: International Journal of Computer Science in Sport 8.2. (pdf)
- West, Julia & Bill, Karen & Martin, Louise (2010): What constitutes Research Ethics in Sport and Exercise Science? In: Research Ethics 6.4. 147–53. DOI: 10.1177/174701611000600407
- Williams, Stephen John & Kendall, Lawrence R. (2007): A Profile of Sports Science Research (1983-2003). In: JSAMS 10.4. 193-200. DOI: https://doi.org/10.1016/j.jsams.2006.07.016
Repositories :
F 07
- RMD F 07
-
Faculty 07: Mathematics and Computer Science, Physics, Geography
Mathematics
General Information:
- DeliverMath. (Access)
- Forschungsdaten.info: Physik and Mathematik. (Access) -Ger-
- Koprucki, Thomas & Tabelow, Karsten & Kleinod, Ilka (2016): Mathematical Research Data. In: PAMM 16.1. 959-960. DOI: 10.1002/pamm.201610458
- University of Amsterdam. Data Management in Mathematics. (Access)
- University of Oxford: Mathematical Institute. Research Data Management. (Access)
- Wormack, Ryan P. (2015): Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics. In: PLOS ONE 10.12. DOI: https://doi.org/10.1371/journal.pone.0143460
General Information - Printed media:
- Horton, Nicholas J. & Kleinmann, Ken (2011): Using R for Data Management, Statistical Analysis, and Graphics . Boca Raton u.a.: CRC Press.
- Mitchell, Michael N. (2010): Data Management Using Stata . College Station, Tex.: Stata Press.
- Stata Corporation (2009): Stata Data-Management Reference Manual . College Station, Tex.: Stata Press.
Repositories:
- Banff International Research Station for Mathematical Innovation and Discovery
- Code Ocean
- ETH Zürich Research Collection
- Kaggle
- NASA Prognostics Data Repository
- National Science Digital Library
- Network Repository
- NIST Data Repository
- The Netlib
- The SuiteSparse Matrix Collection
Informatics
General Information:
- DFG Projekt: Zum Zusammenhang von disziplinären Originalitätskonzepten und handlungspraktischen Orientierungen für das Teilen von Daten. (= Research Data. On the Relation between Disciplinary Concepts of Originality and Practical Orientations for Sharing Data). (Access) -Ger-
- eXtensible Markup Language (XML). (Access)
- Fleckenstein, Mike & Fellows, Lorraine (2018): Modern Data Strategy. Cham: Springer International Publishing. (Access via library system JLU )
- Hameurlain, Abdelkader et al. (Hg.) (2015): Transactions on Large-Scale Data- and Knowledge-Centered Systems XX: Special Issue on Advanced Techniques for Big Data Management . Berlin und Heidelberg: Springer. (Access via library system JLU )
- Han, Jiawei, & Kamber, Micheline & Pei, Jian (2012): Data Mining: Concepts and Techniques. Amsterdam: Elsevier. (Access via library system JLU )
- Klemens, Ben (2008): Modeling with Data: Tools and Techniques for Scientific Computing . Princeton: Princeton UP. (Access via library system JLU )
- Krogstie, John & Reijers; Hajo A. (Hg.) (2018): Advanced Information Systems Engineering . 30 th International Conference. Cham: Springer International Publishing. (access via library system JLU )
- Liu, Ling & Özsu, M. Tamer (Hg.) (2018): Encyclopedia of Database Systems. New York: Springer NY. (Access via library system JLU )
- Medizininformatikinitiative (= Medical Informatics Initiative). (Access) -Ger-
- Meineke, Frank & Schmitz, Janine & Lindstädt, Birte: Forschungsdatenmanagement als Aufgabe in der Medizinischen Informatik. (= Research Data Management as a Task of Medical Informatics). (pdf) -Ger-
- Newman, Anna (2016): Modeling Mortality Data: A Case Study in Data Management for Computer Science Research. University of Massachusetts and New England Area Librarian e-Science Symposium. DOI: https://doi.org/10.13028/s4hs-yk24
- Oxford LibGuides. Computer Science: Managing your Research Data. (Access)
- Rachel, Thomas (2016): Big Data und Medizininformatik: Wie kann die intelligente Verknüpfung von Patienten- und Forschungsdaten gelingen? (= Big Data and Medical Informatics: How to connect Patient and Research Data intelligently?). Keynote. (Access) -Ger-
- Resource Description Framework (RDF). (Access)
- University of Amsterdam: Data Management in Computer Science. (Access)
General Information - Printed media:
- Grosky, William I. (Hg.) (2002): Theory and Practice of Informatics. 29th Conference on Current Trends in Theory and Practice of Informatics . Berlin u.a.: Springer.
- Horton, Nicholas J. & Kleinmann, Ken (2011): Using R for Data Management, Statistical Analysis, and Graphics . Boca Raton u.a.: CRC Press.
- Mitchell, Michael N. (2010): Data Management Using Stata . College Station, Tex.: Stata Press.
- Stata Corporation (2009): Stata Data-Management Reference Manual . College Station, Tex.: Stata Press.
Repositories:
- AIDA Data Hub
- Banff International Research Station for Mathematical Innovation and Discovery
- CiteSeerX
- CKAN @ IoT Lab
- Code Ocean
- CRAWDAD Community Resource for Archiving Wireless Data At Dartmouth
- DeiC Data
- FLOSSmole
- GTS AI Data Collection
- IMPACT Information Marketplace for Policy and Analysis of Cyber-Risk & Trust
- Informatics Research Data Repository
- Kaggle
- KNB The Knowledge Network for Biocomplexity
- Launchpad
- NASA Prognostics Data Repository
- National Science Digital Library
- NIST Data Discovery
- OpenML
- Spec Patterns
- Stanford Network Analysis Project
- The Netlib
- UCI Machine Learning Repository
- Unidata Internet Data Distribution
Physics
General Information:
- ATLAS (A Toroidal LHC ApparatuS) Data Access Policy. (pdf)
- Atmospheric Chemistry and Physics: Data policy. (Access)
- Compact Muon Solenoid (CMS) Data Preservation, Reuse and Open Access Policy. (pdf)
- Data Access Policy for Large Hadron Collider beauty (LHCb). (pdf)
- DPHEP Data Preservation in High Energy Physics
- ESCAPE: Launch of the European Project for Data Management in Astronomy (2018). (Access)
- EU Particle Physics & Astronomy commit to the Research Data Revolution making the European Open Science Cloud a Reality (2019). (Access)
- Forschungsdaten.info. Physics and Mathematics. (Access) -Ger-
- Frahm, Holger (2019): Forschungsdaten effizient managen. (= Managing Research Data efficiently). In: Physik Journal 18.3. S.3. (Access) -Ger-
- Goodger, Joanna & Worthington, William (2014): Research Data Management Training for the whole Project Lifecycle in Physics & Astronomy Research. Final Report. (pdf)
- Han, Mincheol et al. (2011): New Data Libraries and Physics Data Management Tools. In: Journal of Physics: Conference Series 331. (pdf)
- Han, Mincheol et al. (2010): Physics Data Management Tools: Computational Evolutions and Benchmarks. In: Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA + MC2010). (pdf)
- Helbig, Kerstin et al. (2018): Forschungsdaten in der Physik. (= Research Data in Physics). Video. Humboldt-Universität zu Berlin, Media Repository. DOI: https://doi.org/10.18450/dataman/95 -Ger-
-
Neuroth, Heike et al. (2012):
Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme.
Kapitel 13: Teilchenphysik
. (= Long-Term Archiving of Research Data: Assessing the status quo: Chapter 13: Particle Physics).
(pdf)
-Ger-
Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kapitel 14: Astronomie und Astrophysik . (= Long-Term Archiving of Research Data: Assessing the status quo: Chapter 14: Astronomy and Astrophysics). (pdf) -Ger- - University of Amsterdam: Data Management in Physics. (Access)
- Wambsganß, Joachim (2016): Open Data und Forschungsdatenmanagement in Physik und Astronomie: Warum, wozu und wie? (= Open Data and Research Data Management in Physics and Astronomy: Why, what for and how?). Presentation. (pdf) -Ger-
- Wormack, Ryan P. (2015): Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics. In: PLOS ONE 10.12. e0143460 DOI: https://doi.org/10.1371/journal.pone.0143460
Repositories:
- AMODS Atomic Molecular and Optical Database Systems
- ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer
- Astrophysics Source Code Library
- ATNF Pulsar Catalogue
- CDA Chandra Data Archive
- CHIANTI An Atomic Database for Spectroscopic Diagnostics of Astrophysical Plasmas
- CSD Cambridge Structural Database
- Digital Lunar Orbiter Photograohic Atlas of the Moon
- EDC EUROLAS Data Center
- ENDF Evaluated Nuclear Data File
- ETH Zürich Research Collection
- Galaxy Zoo
- GENIE General Internet Search Engine for Atomic Data
- GEOS Space Environment Monitor
- HEPData High-Energy-Physics Data
- HyperLeda
- International Service of Geomagnetic Indices
- Meteoritical Bulletin Database
- MPDS Materials Platform for Data Science
- nanoHUB
- NASA/IPAC Extragalactic Database
- NASA Life Sciences Data Archive
- NIST Physical Reference Data
- Ozone Mapping and Profiler Suite
- R.L. Kelly atomic and ionic linelist
- SAO/NASA Astrophysics Data System Dataverse
- Space Phyics Data Facility
- The ACE Science Center
- Ukrainian Geospatial Data Center
- VALD3 Vienna Atomic Line Database
- VAMDC Virtual Atomic and Molecular Data Centre Portal
- World Data Centre for Space Weather
- 4TU.Centre for Research Data
Geography
General Information:
- American Geophysical Union (2019): Geoscience Data Group urges all Scientific Disciplines to make Data open and accessible. ScienceDaily . 4 June. (Access)
- ArcGIS Pro: Überblick über die Toolbox "Data Management". (= Overview on the Toolbox "Data Management"). (Access) -Ger-
- Collaborative Research Centre 806 Database. (Access)
- Dallmeier-Tiessen, Sünje & Pfeiffenberger, Hans (2009): Umgang mit Forschungsdaten in den Geowissenschaften- Ein Blick in die Praxis-. (= Handling Research Data in the Earth Sciences - A Practical Approach). (pdf) -Ger-
- Earth Data: Earth Science Data Systems (ESDS) Program. (Access)
- EOS (2018): Advancing FAIR Data in Earth, Space, and Environmental Science. (Access)
- EWIG (Entwicklung von Workflowkomponenten für die Langzeitarchivierung von Forschungsdaten in den Geowissenschaften) (= Developing Workflow Components for the Long-Term Archiving of Research Data in Earth Science) (2014): Einstieg ins Forschungsdatenmanagement in den Geowissenschaften. (= Introduction to Research Data Management in the Earth Sciences). (pdf) -Ger-
- FIDGeo: Frequently Asked Questions. (Access)
- FIDGeo: Research Data. (Access)
- Gil, Yolanda et al. (2016): Toward the Geoscience Paper of the Future: Best Practices for Documenting and Sharing Research from Data to Software to Provenance. In: Earth and Space Science 3.10. 388–415. DOI: 10.1002/2015EA000136
- Goldstein, Justin C. & Mayernik, Matthew S. & Ramapriyan, Hampapuram K. (2017): Identifiers for Earth Science Data Sets: Where we have been and where we need to go. In: Data Science Journal 16.23. DOI: http://doi.org/10.5334/dsj-2017-023
- Guo, Huadong (2017): Big Earth data: A New Frontier in Earth and Information sciences. In: Big Earth Data 1.1-2. 4-20. DOI: 10.1080/20964471.2017.1403062
- Helbig, Kerstin et al. (2015): Forschungsdaten-Knowhow für Geographen. (= Research Data Knowhow for Geographers). Presentation. Zenodo. DOI: http://doi.org/10.5281/zenodo.18225 -Ger-
- Helbig, Kerstin (2015): Research Data Management Training for Geographers: First Impressions. In: International Journal of Geo-Information 5.40. DOI: 10.3390/ijgi5040040
- Kempler, Steve & Mathews, Tiffany (2017): Earth Science Data Analytics: Definitions, Techniques and Skills. In: Data Science Journal 16.6. DOI: https://doi.org/10.5334/dsj-2017-006
- Klump, Jens (2012): Forschungsdatenmanagement in den Geowissenschaften. (= Research Data Management in the Earth Sciences). (pdf) -Ger-
- Lotz, Christian (2018): Forschungsdaten in der historischen Geografie und Kartografie. (= Research Data in Historical Geography and Cartography). Presentation. (pdf) -Ger-
- Miller, Harvey J. & Goodchild, Michael F. (2014): Data-Driven Geography. In: GeoJournal 80.4. 449-461. DOI: 10.1007/s10708-014-9602-6 (Alternative access via library system JLU )
- National Academy of Science (2002): Assessment of the Usefulness and Availability of NASA’s Earth and Space Science Mission Data . Chapter 4: Strategies for Managing Earth and Space Science Data. Washington: National Academy Press. (Access)
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kapitel 9: Geowissenschaften. (= Long-Term Archiving of Research Data: Assessing the status quo. Chapter 9: Earth Science).Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- Wade, Bishop Bradley & Hank, Carolyn (2018): Earth Science Data Management: Mapping Actual Tasks to conceptual Actions in the Curation Lifecycle Model. In: Chowdhury G. et al. (Eds). Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science 10766. DOI: https://doi.org/10.1007/978-3-319-78105-1_67
- Wagner, Bianca (2016): Forschungsdatenmanagment in den (Göttinger) Geowissenschaften. (= Research Data Management in the (Goettingen) Earth Sciences). (pdf) -Ger-
General Information - Printed media:
- Kappas, Martin (2012): Geographische Informationssysteme. (= Geographic Information Systems) . Braunschweig: Westermann. -Ger-
Repositories:
- ACTRiS Data Centre
- Alternative Fuels Data Center
- CBEO Chesapeake Bay Environmental Observatory
- CIESIN Center for International Earth Science Information Network
- climate4impact
- Collaborative Climate Community Data and Processing Grid
- Copernicus
- EarthChem Library Data Repository for Earth Science Data and other Digital Objects
- EBAS
- EOL Encyclopedia of Life
- ESSD Earth System Science Data, First Journal for Research Data in Earth Science Listed in the Science Citation Index
- GeoReM Geological and Environmental Reference Materials
- GeoPortal.rlp
- GFZ Data Services
- Historical hydrographic Data from BSH
- IISD Experimental Lakes Area
- ISCCP International Satellite Cloud Climatology Project
- Italian National Biodiversity Network
- National Science Foundation Polar Programs UV Monitoring Network
- NCEDC Northern California Earthquake Data Center
- Neotoma Paleoecology Database
- New Mexico Bureau of Geology & Mineral Resources
- NGDC National Geoscience Data Centre
- Open Topography
- Ozone Mapping and Profiler Suite
- PANGAEA Open Access Database for Geocoded Data from Earth Science and Life Sciences
- Precipitation Measurement Missions
- RRUFF Project
- SCEC Southern California Earthquake Center
- Unidata's RAMADDA
- WorldClim - Global Climate Data
- World Ocean Atlas
Controlled Vocabulary/Thesauri:
- TGN Getty Thesaurus of Geographic Names
F 08
- RMD F 08
-
Faculty 08: Biology and Chemistry
Biology
General Information:
- Ake, Hannah et al. (2018): Biological & Chemical Oceanography Data Management Office: A Domain-Specific Repository for Oceanographic Data from around the World. Poster. (pdf)
- Arend, Daniel (2019): Nachhaltige Infrastruktur zur Forschungsdatenpublikation am Beispiel von Hochdurchsatz-Pflanzenphänotypisierungsdaten . (= Sustainable Infrastructure for Research Data Publications Using the Example of High-Throughput Plant Phenotyping Data). Marburg: Otto von Guericke University Library. Dissertation. DOI: http://dx.doi.org/10.25673/13463 -Ger-
- Cannataro, Mario & Guzzi, Pietro H. (2012): Data Management of Protein Interaction Networks. Hoboken: Wiley. (Access via library system JLU )
- Chiarelli, Andrea & Johnson, Rob (2017): Research Data Management Case Study – Biological Data Mining. DOI: http://doi.org/10.5281/zenodo.818423
- DFG Project Team “Biodiversity Data“ (2015): Guidelines on the Handling of Research Data in Biodiversity Research. (pdf)
- Diepenbroek, Michael et al. (2014): Towards an Integrated Biodiversity and Ecological Research Data Management and Archiving Platform: The German Federation for the Curation of Biological Data (GFBio). Conference Paper (Access)
- Fender, Ann-Catrin (2015): Repositorien für Forschungsdaten am Beispiel des Faches Biologie: Ein neues Aufgabenfeld für Bibliotheken. (= Repositories for Research Data Using the Example of the Discipline Biology: A New Task for Libraries?) In: Perspektive Bibliothek 4.2. 60-86. DOI: 10.11588/pb.2015.2.26272 -Ger-
- Forschungsdaten.org: GFBio. (Access) -Ger-
- Gemeinholzer, Birgit et al. (2019): Big Data in der Biologie: Kein Problem mehr! (= Big Data in the Discipline Biology: No Problem anymore!) In: Biologie Unserer Zeit 1.49. 59-67. DOI: https://doi.org/10.1002/biuz.201910668 -Ger-
- Helbig, Kerstin et al. (2018): Forschungsdaten in der Biologie. (= Research Data in the Discipline Biology). Video. Humboldt-University Berlin, Media-Repository. DOI: https://doi.org/10.18450/dataman/97 -Ger-
- Helbig, Kerstin & Aust, Pamela (2016): Forschungsdatenmanagement für Agrarwissenschaftler und Biologen. (= Research Data Management for Agronomists and Biologists). DOI: http://doi.org/10.5281/zenodo.53196 -Ger-
- Lapatas, Vasileios et al. (2015): Data Integration in Biological Research: An Overview. In: Journal of Biological Research-Thessaloniki 22.9. DOI: 10.1186/s40709-015-0032-5
- Lesk, Arthur M. (Hg.) (2005): Database Annotation in Molecular Biology . Chichester: Wiley. (Access via library system JLU )
- Li, Wenkui (Hg.) (2013): Handbook of LC-MS Bioanalysis. Best Practices, Experimental Protocols, and Regulations . Hoboken: Wiley. (Access via library system JLU )
- Löffler, Felicitas & Astor, Tina & Müller-Birn, Claudia (2018): Unfolding Existing Data Publication Practice in Research Data Workflows in the Biological and Environmental Sciences – First Results from a Survey. Presentation. DOI: 10.22032/dbt.37803
- Management Molekularer Daten im Research Data Life Cycle (MaMoDaR). (= Managing Molecular Data within the Research Data Life Cycle). Project. (Access) -Ger-
- Quast, Björn (2015): Datenmanagement-Planung im GFBio Projekt – Eine Standortbestimmung. (= Data Management Planning in the GFBio Project – An Assessment of the Situation). Presentation. (pdf) -Ger-
- Renaut, Sebastien et al. (2018): Management, Archiving, and Sharing for Biologists and the Role of Research Institutions in the Technology-Oriented Age. In: BioScience 68.6. 400-411. DOI: 10.1093/biosci/biy038 (Alternative access via library system JLU )
- Soler, Alejandra & Ort, Mara & Steckel, Juliane (2016): An Introduction to Data Management (GFBio). (Access)
- Urbano, Ferdinando & Cagnacci, Francesca (Hg.) (2014): Spatial Database for GPS Wildlife Tracking Data. A Practical Guide to Creating a Data Management System with PostgreSQL/PostGIS and R . Cham: Springer. (Access via library system JLU )
- Wormack, Ryan P. (2015): Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics. In: PLOS ONE 10.12. DOI: https://doi.org/10.1371/journal.pone.0143460
General Information - Printed media:
- Breitsameter, L. et al. (2016): Verfügbarmachung und Nachnutzung von Forschungsdaten – schlummerndes Potential in der agrarwissenschaftlichen Forschung. (= Making Research Data Accessible and Reusable – Unlocked Potential in Agronomic Research). Klimawandel und Qualität: 59. Jahrestagung der Gesellschaft für Pflanzenbauwissenschaften e.V. Henning Kage, Klaus Sieling und L. Francke-Weltmann (Hg.). 82-83. Göttingen: Verlag Liddy Halm.
Repositories:
- Autism Chromosome Rearrangement Database
- BaAMPs Biofilm-Active AMPs Database
- BacDive Bacterial Diversity Metadatabase
- BioModels
- CancerData.org
- Cell Image Library
- CorrDB Animal Trait Correlation Database
- CORUM Comprehensive Resource of Mammalian Protein Complexes
- Database of Genomic Variants archive
- dbSNP
- dbVar
- DEPOD DEPhOsphorylation Database
- DNA Databank of Japan
- DNASU Plasmid Repository
- doRiNA
- EchoBASE
- EMBL-EBI European Molecular Biology Laboratory – European Bioinformatics Institute EBI
- European Nucleotide Archive
- European Variation Archive
- FlyBase
- GenBank
- GFBio
- HAGR Human Ageing Genomic Resources
- HIstome The Histone Infobase
- HMS Harvard Medical School, Library of Integrated Network-Based Cellular Signatures Database
- Human Intermediate Filament Database
- Integrated Relational Enzyme database
- INTREPID Bioinformatics
- iRefWeb Interaction Reference Index Web Interface
- JCB Journal of Cell Biology
- KiMoSys Kinetic Models of Biological Systems
- MGnify protein database
- MTB Mouse Tumor Biology Database
- NASA Life Sciences Data Archive
- NCBI Assembly
- NCBI Nucleotide
- NCBI PopSet
- NCBI Trace Archives
- OMIA Online Mendelian Inheritance in Animals
- Open Tree of Life
- Oral Cancer Gene Database
- Pangaea Open Access Database for Geocoded Data from Earth Science and Life Sciences
- Pfam Protein Families
- Plant Organelles Database 3
- PMN Plant Metabolic Network
- Pseudobase
- pSILAC
- Screening Unit Berlin-Buch
- SRA Sequence Read Archive
- SuperTarget
- STRING Known and Predicted Protein-Protein Interactions
- SWATHAtlas
- UniProt Knowledgebase
- WormBase
- Xenbase
Chemistry
General Information:
- Adams, Sam et al. (2011): The Quixote Project: Collaborative and Open Quantum Chemistry Data Management in the Internet Age. In: Journal of Cheminformatics 3.38. DOI: https://doi.org/10.1186/1758-2946-3-38
- Castle, Clair (2017): Adapting Central RDM Messages to Discipline-Specific Needs at the Department of Chemistry, University of Cambridge. IFLA 2017 Satellite Meeting. (Access)
- Chen, Xiujuan & Wu, Ming (2017): Survey on the Needs for Chemistry Research Data Management and Sharing. In: The Journal of Academic Librarianship 43.4. 346-353. DOI: https://doi.org/10.1016/j.acalib.2017.06.006
- Collins, Anna (2015): Research Data Management - Managing your Digital Research Data: Example Presentation to Department of Chemistry 2012. Presentation. (Access)
- Example Data Management Plan: Chemistry. (Access)
- Hartshorn, Richard (2017): Research Data, Big Data, and Chemistry. In: Chemistry International 39.3. DOI: https://doi.org/10.1515/ci-2017-0301
- Helbig, Kerstin et al. (2018): Forschungsdaten in der Chemie. (= Research Data in the Discipline of Chemistry). Video. Humboldt-Universität zu Berlin, Medien-Repositorium. https://doi.org/10.18450/dataman/96 -Ger-
- Jung, Nicole et al. (2017): Erfassung und Speicherung von Forschungsdaten im Fachbereich Chemie: Bereitstellung moderner Forschungsinfrastrukturen durch ein elektronisches Labjournal mit Repositorium-Anbindung. (= Assessing and Storing Research Data in the Discipline of Chemistry: Providing Modern Research Data Infrastructures via an Electronic Lab Notebook with Repository-Connection). E-Science-Tage 2017: Forschungsdaten managen. J. Kratzke (Hg.). 127-136. Heidelberg: heiBOOKS. DOI:10.5445/IR/1000078209 -Ger-
- Jung, Nicole & Lütjohann, Dominic (2013): Chemie auf der Spitze des Eisbergs: Zu viele Forschungsdaten gehen bislang unter! (= Chemistry at the Tip of the iceberg: Too much Research Data has been lost so far!) In: Chemie in unserer Zeit 47.6. 334-335. DOI: https://doi.org/10.1002/ciuz.201390062 -Ger-
- Nationale Forschungsdateninfrastruktur für die Chemie. (= National Research Data Infrastructures for Chemistry). (Access) -Ger-
- Scotti, Marcus Tullius et al. (2018): „SistematX, an Online Web-Based Cheminformatics Tool for Data Management of Secondary Metabolites. In: Molecules 23.1. 103. DOI: https://dx.doi.org/10.3390%2Fmolecules23010103
- Reisner, Barbara A. & Vaughan, K. T. L. & Shorish, Yasmeen L. (2014): Making Data Management Accessible in the Undergraduate Chemistry Curriculum. In: Journal of Chemical Education 91.11. 1943-1946. DOI: https://doi.org/10.1021/ed500099h
- Rzepa, Henry S. & Mclean, Andrew & Harvey, Matthew J. (2016): InChl As a Research Data Management Tool. In: Chemistry International 38. 3-4. 24-26. DOI: https://doi.org/10.1515/ci-2016-3-408
- Thielen, Joanna & Li, Ye (2015): Profiling Common Types of Research Data and Methods published by Organic Synthesis Chemists at the University of Michigan. Paper presented at the SLA 2015 Annual Conference & Info Expo, Boston, MA. (Access)
- Wormack, Ryan P. (2015): Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics. In: PLOS ONE 10.12: e0143460 DOI: https://doi.org/10.1371/journal.pone.0143460
- Xu, Hong & Ishida, Mayu & Wang, Minglu (2016): A Data Management Plan for Effects of Particle Size on Physical and Chemical Properties of Mine Wastes. In: Research Ideas and Outcomes 2:e11065. DOI: 10.3897/rio.2.e11065
Repositories:
- AFCDB Alberta Food Compostition Database
- caNanoLab
- CDS National Chemical Database Service
- ChEMBL
- Chemotion
- Chempound
- ChemSpider
- Citrination
- CSD The Cambridge Structural Database
- The Cristal Structure Depot
- DOE Data Explorer
- DrugBank
- EarthChem
- eCrystals
- EDGAR Emissions Database for Global Atmospheric Research
- FooDB
- ICSD Inorganic Crystal Structure Database
- ioChem-BD
- ISCCP International Satellite and Cloud Climatology Project
- MCHF/MCDHF Database
- nanoHUB
- National Atmospheric Deposition Program
- NIST Ultraviolet Spectrum of Platinum Lamp
- NMRShiftDB
- NOMAD Repository
- PubChem
- Reciprocal Net
- RRUFF Project
- SDBS Spectral Database for Organic Compounds
- STRENDA Standards for Reporting Enzymology Data
- The Binding Database
- The Canadian National Atmospheric Chemistry Database
- World Data Centre for Precipitation Chemistry
- YMDB The Yeast Metabolome Database
- ZINC
F 09
- RDM F 09
-
Faculty 09: Agricultural Sciences, Nutritional Sciences and Environmental Management
Agricultural Sciences, Nutritional Sciences and Environmental Management
General Information:
- Forschungsdaten.info: Biologische Forschung. (= Biological Research). (Access) -Ger-
- Principles for the Responsible Handling of Research Data at the Helmholtz Centre for Environmental Research GmbH – UFZ. (Access)
-
Hartmann, Niklas K. (2019):
Perso
nenbezogene Forschungsdaten in unverdächtigen
Disziplinen:
Das Beispiel der Erd
-
,
Umwelt
-
und Agrar
wissenschaften
. (= Personal Research Data in Unsuspicious Disciplines: The Examples of Earth, Environmental and Agricultural Science). Preprint.
(pdf)
-Ger-
- Helbig, Kerstin & Aust, Pamela (2016): Forschungsdatenmanagement für Agrarwissenschaftler und Biologen. (= Research Data Management for Agronomists and Biologists). (pdf) -Ger-
- Interest Group in Agricultural Data (Access) - Möglichkeiten und Lehren des Teilens und Wiederverwendens von Daten. (= Opportunities and Lessons Learnt from Sharing and Re-Using Data). (Access) -Ger-
- Lindstädt, Birte (2016): Forschungsdaten in den Agrarwissenschaften: Management und Publikation. (= Research Data in Agricultural Sciences: Management and Publishing). In: Intelligente Systeme – Stand der Technik und neue Möglichkeiten, Lecture Notes in Informatics (LNI), 15. Ed. A. Ruckelshausen et al. (pdf) -Ger-
- NFDI4Life. (Access)
- Online Course “Open Data Management in Agriculture and Nutrition” of the GODAN Action Projects. (Access)
- Schmidt, Birgit & Gemeinholzer, Birgit & Treloar, Andrew (2016): Open Data in Global Environmental Research: The Belmont Forum’s Open Data Survey. In: PLOS ONE 11.1: e0146695. DOI: https://doi.org/10.1371/journal.pone.0146695
- Stahl, Ulrike (2017): Research Data Publication – Why, how and where? 10 th Young Scientists Meeting: 8 th -10 th November 2017 in Siebeldingen: Abstracts. Ed. Young Scientists Meeting, Julius Kühn-Institut. Ribbesbüttel: Saphir Verlag. S. 12-13. (Access via library system JLU )
- Wong, Ka-Chung (Hg.) (2016): Big Data Analytics in Genomics . Cham: Springer International Publishing. Ebook. (Access via library system JLU )
Important Standards:
Repositories:
- Source: Helbig, Kerstin und Aust, Pamela (2016). Forschungsdatenmanagement für Agrarwissenschaftler und Biologen (=Research Data Management for Agricultural Scientists and Biologists) (Access) -Ger-
- Dryad Curated Repository
- GFBio German Federation for Biological Data: Data on the Environment anx Biodiversity
- iDigBio Integrated Digitized Biocollections: Digitalized Biodiversity Collections
- Morphbank Biological Imaging
- PANGEA Open Access Database, contains Species- and Observation-Based Biodiversity Data
- Symbiota Open Source Database, contains Species- and Observation-Based Biodiversity Data
- VertNet : Contains Biodiversity Data
- ZALF (Leibniz Centre for Agricultural Landscape Research) Open Research Data Portal: Platform for Freely Accessible Landscape Research Data
- Agricultural Research Data Book (India)
- Agri-Environmental Research Data Repository Dataverse of University of Guelph
- EDIT Platform for Cybertaxonomy an Integrated Software Environment for Biodiversity Research Data Management
- Journal of Agricultural Science and Research
- Klimawandel, Forschung und Daten -Ger-
- Open Data Journal for Agricultural Research
Controlled Vokabulary/Thesauri:
- AGROVOC Agricultural Information Management Standard
F 10
- RDM F 10
-
Faculty 10: Veterinary Medicine
Veterinary Medicine
General Informationen:
- ARRIVE: Animal Research: Reporting of In Vivo Experiments. Guidelines. (pdf)
- ASPCAPro (American Society for the Prevention of Cruelty to Animals): How to Collect Animal Data. (Access)
- Series of interviews by RDM Supports der Universität von Utrecht: "I'm a Vet, not a Data Manager". (Access)
- Kerby, Erin (2015): Research Data Practices in Veterinary Medicine: A Case Study. In: Journal of eScience Librarianship 4.1. DOI: 10.7191/jeslib.2015.1073
- Kerby, Erin (2016): Research Data Services in Veterinary Medicine Libraries. In: Journal of the Medical Library Association: JMLA 104.4. 305-308. DOI: 10.3163/1536-5050.104.4.010
- NFDI4Life
- Omary, M. Bishr et al. (2016): Not All Mice Are the Same: Standardization of Animal Research Data Presentation. In: Gastroenterology 150.7. 1503-1504. DOI: https://doi.org/10.1053/j.gastro.2016.03.034
- STROBE-Vet (Strengthening the Reporting of Observational Studies in Epidemiology). Statement. (Access)
Repositories :
- Canine Inherited Disorders Database
- EQUATOR Enhancing the QUAlity and Transparency Of health Research
- IMPC International Mouse Phenotyping Consortium
- OFA Canine Health Information Centre
- PetPoint Webbased Data Management System
- Species 360 Wildlife Research Data
F 11
- RDM F 11
-
Faculty 11: Medicine
Medicine
General Information:
- Adibuzzaman, Mohammad et al. (2018): Big Data in Healthcare – the Promises, Challenges and Opportunities from a Research Perspective: A Case Study with a Model Database. In: AMIA Annu Symp Proc . 2017. 384-392. (Access)
- AHRQ Agency for Healthcare Research and Quality
- Bak, Marieke (2018): Big Data, Big Ethics: How to handle Research Data from Medical Emergency Settings? (Access)
- Chen, Ding-Geng (2011): Clinical trial data analysis using R. Bosa Roca, Fla. u.a.: Chapman & Hall.
- Dickmann, Frank (2011): Langzeitarchivierung von Forschungsdaten: Wie geht man mit Peta- und Exabytes um? (= Long-Term Archiving Research Data: How to handle Petabytes and Exabytes?). In: Deutsches Ärzteblatt 108.41. A-2172. (Access) -Ger-
- Donaldson, Mary & Knight, Gareth (2018): London School of Hygiene and Tropical Medicine – Case Study in Funding Research Data Management. Zenodo. DOI: 10.5281/zenodo.1408822
- Floca, Ralf (2012): Medizinische Forschungsdaten. Das Spannungsfeld zwischen Datenschutz und moderner Medizinforschung am Beispiel der Krebsforschung. (= Medical Research Data. The Conflict between Data Protection and Modern Medical Research Using the Example of Cancer Research). (pdf) -Ger-
- Hahn, Udo et al. (2018): 3000PA-Towards a National Reference Corpus of German Clinical Language . In: Studies in Health Technology and Informatics 247. 26-30. (pdf)
- HDRUK Health Data Research UK
- Institute of Medicine (2013): Sharing Clinical Research Data: Workshop Summary. Washington: National Academies Press. Ebook. (Access) (Alternative Access via library system JLU )
- Jacoby, Bart & Popma, Jean (2019): Medical Research, Big Data and the Need for Privacy by Design . In: Big Data & Society 6.1. 1–5. DOI: https://doi.org/10.1177/2053951718824352
- Just, Eric & Whitaker, Sean (2015): The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed. (Access)
- Kuchinke, Wolfgang (2014): Umgang mit Daten in der Medizinischen Forschung – Bedeutung von Datenbrücken. Potsdam: RDA Deutschland Treffen. (= Handling Data in Medicial Research – the Importance of Data Bridges). (pdf) -Ger-
- Lindstädt, Birte (2017): Forschungsdaten in der Medizin: Management und Publikation. (= Research Data in the Discipline of Medicine: Management and Publication). In: Klasse statt Masse – wider die wertlose Wissenschaft. 18. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. Hamburg, 09.-11.03.2017. Düsseldorf: German Medical Science GMS Publishing House. DOI: https://dx.doi.org/10.3205/17ebm085 -Ger-
- Müller, Lars et al. (2013): Praxis im Umgang mit medizinischen Forschungsdaten: DCT-Befragungsergebnisse. (= Practice in Handling Medical Research Data: DCT-Survey Results). Potsdam: Fachhochschule Potsdam. (Access) -Ger-
- Naudet, Florian et al. (2018): Data Sharing and Reanalysis of Randomized Controlled Trials in Leading Biomedical Journals with a Full Data Sharing Policy: Survey of Studies published in The BMJ and PLOS Medicine. In: The BMJ 2018 360.k400. DOI: https://doi.org/10.1136/bmj.k400
- Neuroth, Heike et al. (2012): Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme. Kapitel 12: Medizin. (= Long-Term Archiving Research Data. Assessing the Status Quo. Chapter 12: Medicine) . Boizenburg: Verlag Werner Hülsbusch. Ebook. (pdf) -Ger-
- NFDI4Life
- Nonnemacher, Michael & Nasseh, Daniel & Stausberg, Jürgen (2014): Datenqualität in der medizinischen Forschung: Leitlinie zum adaptiven Management von Datenqualität in Kohortenstudien und Registern . (= Data Quality in Medical Research: Guidelines for Adaptive Data Quality Management in Cohort Studies and Registers). Berlin: Med.-Wiss. Verl- Ges. (Access via library system JLU ) -Ger-
- Packer, Milton (2018): Data Sharing in Medical Research. In: BMJ-BRITISH MEDICAL JOURNAL 2018 360.k510. DOI: https://doi.org/10.1136/bmj.k510 (Alternative Access via library system JLU )
- Publisso Open Access publishing Platform for Life Sciences
- SMITH Smart Medical Information Technology for Healthcare
- TMF and TOOLPOOL Plattformen zum FDM in der Gesundheitsforschung (= Platform on RDM in Health Research) -Ger-
- TMF-Workshop (2010): Langzeitarchivierung von medizinischen Forschungsdaten. (= Long-Term Archiving Medical Research Data). (pdf) -Ger-
- MRC Medical Research Council
- Winter, Alfred et al. (2018): Smart Medical Information Technology for Healthcare (SMITH). Data Integration based on Interoperability Standards. In: Methods Inf Med. 2018 57. 92-105. (pdf)
- ZB MED – Leibniz-Informationszentrum Lebenswissenschaften. Workshop: Forschungsdatenmanagement als Aufgabe der Medizinischen Informatik. (= Research Data Management as a Task for Medical Informatics). DOI: https://dx.doi.org/10.3205/15gmds211 -Ger-
- Zhang, Luxia et al. (2018): Big Data and Medical Research in China. In: BMJ (Clinical research ed.) 360.j5910. DOI: 10.1136/bmj.j5910
Repositories :
- Biogrid Australia
- ClinicalCodes.org
- Clinical Data Repository of University of Minnesota
- HSDB Hazardous Substances Data Base (Access via PubChem , Explanations to that)
- Health Sciences Library der University of Washington
- OMIM Online Mendelian Inheritance in Man
- Pediatric Surgery @ Brown
- Prometheus Research
- TOXMAP USA (underlying Data only):
- Government of Canada National Pollutant Release Inventory (NPRI)
- U.S. Census Bureau
- U.S. EPA Clean Air Markets Program
- U.S. EPA Geospatial Applications
- U.S. EPA Facilities Registry System (FRS)
- U.S. EPA Superfund Program
- U.S. EPA Toxics Release Program (TRI)
- U.S. NIH NCI Surveillance, Epidemiology, and End Results Program (SEER)
- U.S. Nuclear Regulatory Commission (NRC)
- Viral Bioinformatics Research Centre
Disclaimer: No legally binding information! Please always contact the legal department of your institution for specific legal questions and for legally binding information and a data protection officer of your institution for questions regarding data protection.
Further information on research data management and law
What is personal data?
According to the General Data Protection Regulation ( GDPR ), personal data “means any information relating to an identified or identifiable natural person [...]; an identifiable natural person is one who can be identified, directly or indirectly , in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person” ( Art. 4 No. 1 GDPR ).
If, for example, 20 natural persons are asked about their last vacation destination, this data is not personal data for the time being. However, if the name is also recorded, the vacation country also counts as personal data, since it can now be directly assigned to a specific natural person. In the case of indirect identification, the same rules apply. Indirect identifiers can also be used to uniquely identify individuals, e.g., in the case of a combination of occupation and company, insofar as this occupation is unique to the company (e.g., in the case of jobs with managerial functions). Here is a list of examples of indirect identifiers: first names, place names, street names, states, institutional/organizational affiliations (e.g., employer, school), occupational information, titles and educational degrees, age, time/calendar data, pictures, and voices.
“Processing of personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation” ( Art. 9 No. 1 GDPR ) is considered processing of special categories of personal data and requires special precautions which may have an impact on the possibilities of publishing such data. If you are unsure, always involve a JLU data protection officer and, if necessary, the Ethics Committee ( Faculty 06 / Faculty 11 ).
Notice:
Data are personal if they can be clearly assigned directly or indirectly to a specific natural person. Depending on the type of personal data, however, it may require different levels of protection, which may have an impact on the possibilities for publishing this data.
What do I have to consider with personal data?
Before the survey
First consider whether personal data are absolutely necessary for your research project or whether you could carry out the project without them. If you plan to aggregate your data for later presentation because you want to perform a statistical analysis, you should already think about which of these data can be directly assigned to a natural person before collecting personal data so that you can store them separately and easily delete them later after data aggregation. If the research purpose requires the collection of personal data, these must be handled extremely sensitive in case of processing of special categories of personal data (see Art. 9 GDPR ). Therefore, special regulations must be followed when handling them, i.e. when collecting, processing, evaluating, analyzing, publishing as well as archiving them. Under certain circumstances, they may also stand in the way of publication of the data. In principle, processing requires a legal basis in order to be lawful (see Art. 6 GDPR ).
Decisive for the handling of personal data within the EU is the GDPR , on the federal level this is additionally regulated by the BDSG . For general further information on the topic of which data protection principles should be taken into account in research design and project planning, see here in German language. For further information related to the social and economic sciences, see also the Data Protection Guide by RatSWD .
Survey
If personal data is included in your research project, there are certain regulations that must be followed when it is collected. This ensures that you are legally allowed to evaluate the data you have collected.
If there is no public interest worthy of protection in the research, which outweighs the interests of the individual in accordance with the Federal Data Protection Act ( § 27 BDSG ), an informed consent must be obtained from the data subject prior to collection. As part of this consent, the data subject must be informed fairly, transparently and comprehensively about the collection and processing of his or her data ( information to be provided according to Art. 13 GDPR ). This includes, among other things, the name and contact details of the person responsible, the purpose of the collection and the legal basis for the data processing, but also information about the storage location, who has access to the data and, if applicable, where the data is published. In addition, the data subject must be informed about the storage period of the data, his or her right of rectification and the lack of consequences of refusing or revoking consent, as well as his or her right to information about his or her personal data. An informed declaration of consent has the advantage that the purpose of the survey can be precisely shown to the respondent, but in the case of a strict purpose limitation, it has the disadvantage that this data may only be used for exactly this research question and only within the scope of this survey. Thus, the data cannot be reused by others, even if they could use it to answer other research questions. In addition, any planned publication should be explicitly stated in the consent form. Further information on informed consents can be found, for example, at VerbundFDB (German). Sample informed consent declarations in German and English can be found at Qualiservice in the download area under “Template forms”.
If the specific purpose of the research has not yet been precisely defined, or if you know in advance that the data may also be relevant to other research questions, you can also create a broad consent . Here you have two possible approaches: Either you already list in this consent your possible further settings for which the data will be used or you try to obtain a very general consent, for example, for your discipline or for all further research questions. Keep in mind, however, that the latter will probably only very rarely be signed by the respondent, despite the right of withdrawal. Furthermore, it is still legally unclear how a broad consent must be written in a legally secure way. In the report on the legal framework for research data management , which was produced as part of the DataJus project in 2018, it is recommended, for example, that a “broad consent” should be as specific as possible, i.e., that possible further research questions and follow-up projects should be listed directly and explicitly.
If the personal data are special categories according to Art. 9 GDPR , they must be listed separately and explicitly in the declaration of consent. This includes, for example, ethnic and biometric data as well as information on political opinions.
The data subject also has the right to ask about his personal data at any time and also to request the deletion of the data as well as to object to processing ( Right of Access according to Art. 15 GDPR ). In particular, the name of the contact person, recipients of the data, storage period, processing purpose and categories of personal data must be provided to him or her as part of the Right of Access. Should a data subject make use of his right of rectification, all evaluations made up to the time of the rectification may continue to be used, only no further evaluations may be made with the data of the data subject.
For the researcher, personal data also entails the obligation of the smallest possible amount of data (= principle of data minimization and economy), the shortest possible storage time, as well as the best possible protection of data against loss and misuse (according to Art. 5 GDPR ). Due to the high complexity of data protection aspects when dealing with personal data, it makes sense in any case for researchers to seek legal advice in advance.
Evaluation
If you process personal data, the GDPR requires you to keep a Record of Processing Activities (see Art. 30 GDPR and Recital 82 GDPR ). Further information on this topic is provided here (German) by the Hessian Commissioner for Data Protection and Freedom of Information. If an external provider is commissioned to conduct, for example, a survey or interview a contract processing agreement must be drawn up in accordance with the GDPR. A sample contract can also be found here (German) at the Hessian Commissioner for Data Protection and Freedom of Information.
“The result of processing for statistical purposes is not personal data, but aggregate data” ( Recital 162 GDPR ). These cannot be traced back to individual natural persons, even if personal data of any protection need were included in the original survey.
Regardless of whether aggregation can be performed or is planned, pseudonymization must be performed as early as possible - e.g., as early as the creation of transcripts. If, for example, each respondent is assigned a kind of "identity number," his or her information can be linked to that number rather than to his or her clear name. Pseudonymization thus means that the personal data can't be processed without adding additional information, such as a key list with the mapping clear name<->identification number because information can no longer be assigned to a specific data subject without this additional information. This goes hand in hand with the fact that this additional information must be stored separately and must also be subject to technical and organizational measures so that it is no longer possible for unauthorized persons to perform an assignment (in accordance with Art. 4 No. 5 GDPR ). Pseudonomyization thus protects the assignment through data separation, but preserves it.
If you want to archive your personal data and/or make them available to the public for subsequent use via a repository in form of a publication, it is important to comply with data protection regulations and to protect the personal rights of the persons subject to the study. The openness of access to the data depends on the degree to which the data require protection.
On the one hand, there is the possibility of publication via data centers, to which data can be handed over without anonymization if necessary and which instead use access restrictions depending on the need for protection. This makes sense because many data sets can lose enormous value through anonymization. Examples for those data centers are Qualiservice and GESIS for social science research data or VerbundFDB , which handles the transfer to data centers of empirical educational research data for you. Here you will also find a list of all research data centers accredited by the RatSWD.
However, if you want to publish your data Open Access in a research data repository (e.g. the institutional research data repository of JLU Gießen called JLUdata or the generic repository Zenodo ), your research data must be anonymized. Anonymized data is “information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.” It further states, “To determine whether a natural person is identifiable, account should be taken of all the means reasonably likely to be used, such as singling out, either by the controller or by another person to identify the natural person directly or indirectly. To ascertain whether means are reasonably likely to be used to identify the natural person, account should be taken of all objective factors, such as the costs of and the amount of time required for identification, taking into consideration the available technology at the time of the processing and technological developments” ( Recital 26 GDPR ).
If you want to publish personal data in JLUdata or another repository, these data must be anonymized and the use after the end of the research project must not be excluded in the consent. This is because even anonymous data may not be published if the consent explicitly excludes subsequent use after the end of the project.
Anonymization techniques distinguish between qualitative and quantitative data. VerbundFDB provides anonymization instructions for both qualitative and quantitative data, which you can find here (German). In addition, OpenAIRE offers a tool called Amnesia to help you anonymize your data. The Data Protection Foundation (Stiftung Datenschutz) published a Practice Guide to Anonymising Personal Data at the end of 2022, which you can find here .
Archiving
According to Art. 5 No. 1e DSGVO , it is permitted for research purposes to store personal data for specific purposes even beyond the period of processing, while adhering to security standards to protect the data from misuse.
As a first step the decision tree (German) developed in the DataJus project on data protection issues arising for the publication of research data can help you with legal questions. In addition, a more detailed overview of data protection in research data management can be found in the article by Watteler/Ebel (2019) (German). Leuphana University Lüneburg (German) also offers an overview of questions about legal aspects surrounding research issues, including answers about handling personal data.
Further information on research data management and law
If you are looking for more information on how to handle legally sensitive data in the context of research data management, the free ILIAS self-study unit on research data management provides a good initial overview. At the moment this self-study unit is only available in German. However, a translation is in the making.
To actively spread knowledge on research data management, we offer our own range of training courses. The content of the courses is orientated towards the research data life cycle:
Our training courses cover e.g. handling tools such as data management plans, meeting requirements made by funding organizations and generic research data management. The courses are designed to comply with your demands – feel free to get in touch with us .
In addition, the "HeFDI" project, in which the JLU is also involved, now offers a wide range of training courses and information on various aspects of research data management, which is available to all Hessian universities free of charge and is regularly repeated and also expanded. In the following, we would like to draw your attention to some of the offers from HeFDI. You can register via the linked pages.
HeFDI Data School
In the HeFDI Data School, we offer cross-location and cross-disciplinary training courses on the topic of research data management. The Data School is aimed at doctoral students and research staff at Hessian universities; the training courses take place online and are free of charge. The Data School is divided into basic and focus modules.
The basic modules of the HeFDI Data School serve as a (first) orientation in research data management. Basic terms are explained and common methods and best practices are presented.
The focus modules of the HeFDI Data School offer an in-depth insight into selected sub-areas of research data management. Basic knowledge in FDM is advantageous, but not mandatory.
HeFDI Data Talks
The HeFDI Data Talks are a two-weekly open information and discussion event. Every second Friday, we present online offers and services for data management and discuss current topics. Currently, this includes in particular the consortia of the National Research Data Infrastructure (NFDI) with their offers. However, local and regional services as well as general information and discussions on data management are also addressed in the Data Talks. If you have suggestions or concerns about topics, please feel free to contact the HeFDI office at hefdi@uni-marburg.de .
The Data Talks take place as a web conference and everyone can participate free of charge. At the beginning, the experts provide input for about 30 to 40 minutes, followed by an opportunity for discussion and questions.
Survey: Research Data at JLU Giessen
In July 2016 we have conducted a survey on research data at JLU Giessen. It has given us advice for building up and providing technical, organizational and consulting infrastructures for research data. The results are to support the scientific practice of the researchers at Justus-Liebig-University as well as possible. Thus, researching together and processing data can more easily be achieved.
The questionnaire was created according to a series of comparable surveys, which had been conducted in the years preceding. Particularly worthy of mention are the surveys by TU Berlin (see Simnukovic et. al. 2013 (pdf in german)) and PU Marburg 2015.
Preview of the Survey (Ger)
Raw Data Gained in the Survey (csv) (Ger)
Report on the Survey (Ger)
Needs Assessment Survey on Electronic Laboratory Notebooks (ELNs) at JLU Giessen
In January 2022 we have conducted a Needs Assessment Survey on Electronic Laboratory Notebooks (ELNs) at JLU Giessen. Our main aim was to find out whether there is a need to set up a central ELN system at JLU and, if so, what requirements the researchers in the various departments would have for such a system.
You can find the results report and the related survey data including the questionnaire at the following links:
Results Report of the ELN Needs Assessment Survey
(Ger)
You can reach the Research Data Mangement Team as follows:
Phone: +49 641-99-14013
E-Mail: forschungsdaten@uni-giessen.de
Locally: Room 11a
Address:
University Library
Otto-Behagel-Strasse 8
35394 Giessen