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A curated list of awesome RDM resources for researchers and organisations
https://github.com/UB-Mannheim/awesome-RDM

List: awesome-RDM

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A curated list of awesome RDM resources for researchers and organisations

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README 

        
# Awesome RDM



"Awesome Research Data Management" is  a curated list of awesome RDM resources for researchers and organizations.



## Definitions



"**Research data** are objects that you use and produce during your research life cycle, encompassing datasets, software, code, workflow, models, figures, tables, images and videos, interviews, articles. Data are your research asset." [The Turing Way / Guide for Reproducible Research / Research Data Management / Research Data](https://the-turing-way.netlify.app/reproducible-research/rdm/rdm-data.html)



**Research "data management** refers to the storage, access and preservation of data produced from a given investigation. Data management practices cover the entire lifecycle of the data, from planning the investigation to conducting it, and from backing up data as it is created and used to long term preservation of data deliverables after the research investigation has concluded. Specific activities and issues that fall within the category of data management include: File naming (the proper way to name computer files); data quality control and quality assurance; data access; data documentation (including levels of uncertainty); metadata creation and controlled vocabularies; data storage; data archiving and preservation; data sharing and reuse; data integrity; data security; data privacy; data rights; notebook protocols (lab or field)." [CODATA RDM-Terminology / RDM](https://codata.org/rdm-terminology/research-data-management)



In a wider sense research data management include also research information management and research knowledge management.



## Table of contents



* [General resources](#general-resources)

    * [Registries](#registries)

        * [Registries of Terminologies, Vocabularies, Ontologies](#registries-of-terminologies,-vocabularies,-ontologies)

    * [Toolkits](#toolkits)

    * [Courses](#courses)

    * [Books](#books)

    * [Games](#games)

    * [Wikis](#wikis)

    * [FAIR principles](#fair-principles)

    * [Research data centers](#research-data-centers)

    * [Journals](#journals)

    * [Conferences](#conferences)

* [RDM for researchers](#rdm-for-researchers)

    * [Planning a project](#planning-a-project)

        * [Motivation for RDM](#motivation-for-rdm)

        * [Costing RDM](#costing-rdm)

        * [Writing a project proposal and searching funding](#writing-a-project-proposal-and-searching-funding)

        * [Data management coordination](#data-management-coordination)

        * [Data management planning](#data-management-planning)

        * [Data policies](#data-policies)

        * [Reusing data](#reusing-data)

    * [Executing a project](#executing-a-project)

        * [Collecting data](#collecting-data)

        * [Creating metadata](#creating-metadata)

        * [Organizing data](#organizing-data)

        * [Data storage](#data-storage)

        * [Data backup](#data-backup)

        * [Cleaning data](#cleaning-data)

        * [Data exploration](#data-exploration)

        * [Data interpretation](#data-interpretation)

        * [Anonymising data](#anonymising-data)

        * [Data protection](#data-protection)

        * [Data provenance](#data-provenance)

        * [Legal aspects](#legal-aspects)

    * [Finishing a project](#finishing-a-project)

        * [Sharing data](#sharing-data)

        * [Publishing data](#publishing-data)

        * [Presenting data](#presenting-data)

        * [Data licensing](#data-licensing)

        * [Archiving data](#archiving-data)

* [RDM for organizations](#rdm-for-organizations)

    * [How to develop RDM services](#how-to-develop-rdm-services)

    * [How to choose an RDM repository](#how-to-choose-an-rdm-repository)

    * [Persistent identifiers](#persistent-identifiers)

* [Discipline-specific RDM](#discipline-specific-rdm)

    * [Social and economic data](#social-and-economic-data)

    * [Digital Humanities](#digital-humanities)

* [Discipline-specific tools](#discipline-specific-tools)

    * [Digital Humanities and Social Sciences](#digital-humanities-and-social-sciences)

* [Discipline-specific repositories](#discipline-specific-repositories)

    * [Digital editions](#digital-editions)

* [Domain-specific NFDI consortia](#domain-specific-nfdi-consortia)

    * [NFDI consortia in Humanities and Social Sciences](nfdi-consortia-in-humanities-and-social-sciences)

    * [NFDI consortia in Engineering Sciences](nfdi-consortia-in-engineering-sciences)

    * [NFDI consortia in Life Sciences](nfdi-consortia-in-life-sciences)

    * [NFDI consortia in Natural Sciences](nfdi-consortia-in-natural-sciences)



## General resources



### Registries



* [Research data management (RDM) open training materials](https://zenodo.org/communities/dcc-rdm-training-materials) ZENODO Community

* [Data Management Training (DMT) Clearinghouse](https://dmtclearinghouse.esipfed.org) is a registry for online learning resources focusing on RDM

* [Data Management Skillbuilding Hub](https://dataoneorg.github.io/Education/) is a repository for open educational resources regarding data management

* [FAIRsharing](https://fairsharing.org) is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies

* [re3data](https://www.re3data.org) is a registry of research data repositories



#### Registries of Terminologies, Vocabularies, Ontologies



* [BARTOC registry of terminology registries](https://bartoc.org/registries)

* [The Basel Register of Thesauri, Ontologies and Classifications (BARTOC)](http://bartoc.org) includes all types of KOS in any format, across all subject areas. 

* [FAIRsharing](https://fairsharing.org) is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies

* [Linked Open Vocabularies (LOV)](https://lov.okfn.org/dataset/lov) is a directory of RDF vocabularies 

* [Linked Data Catalogue](http://linkeddatacatalog.dws.informatik.uni-mannheim.de/dataset)



### Toolkits



* [Elixir RDMkit](https://rdmkit.elixir-europe.org) is a RDM toolkit for Life Sciences [[git](https://github.com/elixir-europe/rdmkit)]

* [JISC RDM Toolkit](https://www.jisc.ac.uk/guides/rdm-toolkit)

* [RDMtoolkit](https://guides.library.uwa.edu.au/RDMtoolkit) of the library at the University of Western Australia

* [Data Management Toolkit @ UNH](https://libraryguides.unh.edu/datamanagement) at University of New Hampshire

* [LEARN Toolkit of Best Practice for RDM](https://www.fosteropenscience.eu/content/learn-toolkit-best-practice-research-data-management) by [fosteropenscience](https://www.fosteropenscience.eu)

* [Research Data Toolkit](https://guides.lib.unc.edu/researchdatatoolkit) at UNC library



### Courses



* [Data Management Expert Guide CESSDA](https://dmeg.cessda.eu/Data-Management-Expert-Guide) is an advanced guide designed by European experts for social science researchers

* [Essentials 4 Data Support](https://datasupport.researchdata.nl/en/) is an introductory course

* [MANTRA Research Data Management Training](https://mantra.ed.ac.uk) is a classic online course

* [Datatree - Data Training Engaging End-users](https://datatree.org.uk/course) is a course for research students and early career researchers in the environmental sciences.

* [Research Data Management and Sharing](https://www.coursera.org/learn/data-management) is a Coursera course by Helen Tibbo & Sarah Jones

* [Carpentries-style lesson on Research Data Management](https://scienceparkstudygroup.github.io/research-data-management-lesson) [[git](https://github.com/ScienceParkStudyGroup/research-data-management-lesson)]

* [McMaster RDM Webinars](https://scds.github.io/intro-rdm) [[git](https://github.com/scds/intro-rdm)]

* [Research Data Management with DataLad](https://psychoinformatics-de.github.io/rdm-course) [[git](https://github.com/psychoinformatics-de/rdm-course)]

* [RDM at Griffith Uni Library](https://griffithunilibrary.github.io/research-data-management/) is short guide for self paced RDM [[git](https://github.com/GriffithUniLibrary/research-data-management)]

* [RDM E-Learning Platform](https://www.researchdatamanagement.ch) ist eine E-learning-Webseite zum Thema Forschungsdatenmanagement der HTW Chur und der HEG Genf

* [PARTHENOS training](https://training.parthenos-project.eu/training-modules/) is a set of training modules in digital humanities and research infrastructures

    * [Manage, improve and open up your research and data](http://training.parthenos-project.eu/sample-page/manage-improve-and-open-up-your-research-and-data/)

* [FOSTER open science courses](https://www.fosteropenscience.eu/courses)

    * [Managing and Sharing Research Data](https://www.fosteropenscience.eu/node/2328) is an introductory course

* [Research data bootcamp](https://data.blogs.bristol.ac.uk/bootcamp) is a general online course from University of Bristol

* [Data Management Short Course for Scientists](https://commons.esipfed.org/datamanagementshortcourse) by the ESIP Federation in cooperation with NOAA and the Data Conservancy

* [RDM Knowledge Base](https://www.ruhr-uni-bochum.de/researchdata/index.html) by Uni Bochum

* [EUDAT Training](https://eudat.eu/training/research-data-management)

* [EOSC-Pillar: RDM Training and support catalogue](https://www.eosc-pillar.eu/rdm-training-and-support-catalogue)

* [Library Carpentry: FAIR Data and Software](https://librarycarpentry.org/lc-fair-research/) [[git](https://github.com/LibraryCarpentry/lc-fair-research)]

* [Research Data Management Promotion Materials](https://rdmpromotion.rbind.io) [[git](https://github.com/Bayquiri/RDM-promotion)]

* [Data Steward Certificate Course](https://www.postgraduatecenter.at/en/programs/communication-media/data-steward/) (paid) at the Vienna University Library.

* [openAIRE Workshops](https://www.openaire.eu/workshops) on various open access and open science topics.

* [Essentials 4 Data Support](https://www.surf.nl/en/agenda/essentials-4-data-support-0) course by from Research Data Netherlands (RDNL).



### Books



* [The Turing Way: a how to guide for reproducible data science](https://the-turing-way.netlify.app/welcome.html) [[git](https://github.com/alan-turing-institute/the-turing-way)]

  * [RDM chapter](https://the-turing-way.netlify.app/reproducible-research/rdm.html)

* [A Research Data Management Handbook OpenAIRE](https://www.openaire.eu/rdm-handbook)

* [Research Data Management Handbook](https://www.organisatiegids.universiteitleiden.nl/binaries/content/assets/archeologie/organisatie/icto/rdm-handbook-fda.pdf) by Leiden University Faculty of Archaeology

* [Research Data Management and Data Literacies](https://shop.elsevier.com/books/research-data-management-and-data-literacies/tibor/978-0-12-824475-3) by Koltay Tibor

* [The Data Book: Collection and Management of Research Data](https://www.routledge.com/The-Data-Book-Collection-and-Management-of-Research-Data/Zozus/p/book/9780367736088) by Meredith Zozus

* [Hand-book of the modern development specialist](https://responsibledata.io/resources/handbook) is a Complete, Illustrated Guide to Responsible Data Usage, Manners, and General Deportment

* [Data Management for Social Scientists](https://doi.org/10.1017/9781108990424) by Nils B. Weidmann



### Games



* [RDM Adventure game](https://rdm-games.gitlab.io/rdm-adventure/)

* [Data Horror Escape Room](https://sites.google.com/vu.nl/datahorror/home?pli=1)

* [Research Data Scarytales](https://forschungsdaten-thueringen.de/rdm-scarytales/articles/overview.html)

* [Data Management: The Game](https://sites.google.com/view/data-management-the-game/)



### Wikis



* [www.forschungsdaten.org](https://www.forschungsdaten.org)

* [forschungsdaten.info](https://forschungsdaten.info)



### FAIR principles



* [The FAIR Guiding Principles for scientific data management and stewardship](https://doi.org/10.1038/sdata.2016.18) is a classic paper on FAIR principles

* [FAIR principles](https://www.go-fair.org/fair-principles) at go-FAIR.org with detailed descriptions

* [The FAIR Cookbook](https://faircookbook.elixir-europe.org) [[git](https://github.com/FAIRplus/the-fair-cookbook)]

* [Three-point FAIRification Framework "How to go FAIR"](https://www.go-fair.org/how-to-go-fair) at go-FAIR.org

* [FAIR Maturity Indicators and Tools](https://github.com/FAIRMetrics/Metrics)

* [FAIR Data Week at Uni Mannheim](https://github.com/UB-Mannheim/FAIR-Data-Week)

* ["FAIR Data Resources" group at Zotero](https://www.zotero.org/groups/2345721/fair_data_resources/) by Atif Latif

* [awesome-fair GitHub repo](https://github.com/Materials-Data-Science-and-Informatics/awesome-fair) is a curated list of awesome stuff around the FAIR principles



### Research data centers



* [List of RDC in Germany](https://www.forschungsdaten.org/index.php/FDM-Kontakte)



### Journals



* [Bausteine Forschungsdatenmanagement](https://bausteine-fdm.de) about RDM only

* [IngGrid Journal](https://www.inggrid.org) about data management in engineering science

* [PLOS ONE](https://journals.plos.org/plosone/) interdisciplinary Open-Access-Journal

* [International Journal of Digital Curation](http://www.ijdc.net/index) about digital curation and data management



### Conferences

* [E-Science-Tage](https://e-science-tage.de/en)

* [IDCC (Digital Curation Centre)](https://www.dcc.ac.uk/events/idcc)

* [Research Data Alliance (RDA) Plenary](https://www.rd-alliance.org/plenaries/)

* [RDA DE Tagung](https://www.rda-deutschland.de/events)

* [Open Science Conference](https://www.open-science-conference.eu/)

* [FDM-Kalender von forschungsdaten.info](https://forschungsdaten.info/kalender-index/)



## RDM for researchers



_Use case:_ a researcher wants to plan, run and finish a research project.



### Planning a project



#### Motivation for RDM



* [Benefits of data management](https://dmeg.cessda.eu/Data-Management-Expert-Guide/1.-Plan/Benefits-of-data-management) by CESSDA

* [Benefits of RDM](https://library.up.ac.za/c.php?g=356288&p=2420384) by UP

* [Advantages of research data management](https://ub.fau.de/en/research/data-and-software-in-research/advantages-of-research-data-management) by UB Erlangen-Nürnberg

* [Benefits of good data management](https://www.st-andrews.ac.uk/research/support/open-research/research-data-management/working-with-data/benefits-of-good-data-management) by the University of St Andrews

* [What are the advantages of managing research data?](https://www.ub.tum.de/en/what-are-the-advantages-of-managing-research-data) by UB TUM

* [Benefits of Data Management](https://libguides.ucd.ie/data/benefits) by UCDL

* [Benefits of RDM](https://www.fu-berlin.de/en/sites/forschungsdatenmanagement/ueber-forschungsdaten/vorteile/index.html) by FU Berlin

* [Benefits of Open Data](https://sco.library.emory.edu/research-data-management/open-data/benefits-research.html) by Emory Library



#### Costing RDM



Check out research data center at your university. They will guide you in RDM for free.

* [Cost-benefit analysis for FAIR research data - Cost of not having FAIR research data](https://data.europa.eu/doi/10.2777/02999) by Directorate-General for Research and Innovation (European Commission) and PwC EU Services 

* [Data management costing tool and checklist](https://ukdataservice.ac.uk/app/uploads/costingtool.pdf) by UK Data Service

* [Guide on research data management costs](https://www.lcrdm.nl/files/lcrdm/2020-04/RDM%20and%20Costs_v20160218_EN.pdf) by LCRDM

* [How to identify and assess RDM costs](https://www.openaire.eu/how-to-comply-to-h2020-mandates-rdm-costs) is an OpenAIRE guide for H2020 grants



#### Writing a project proposal and searching funding



* [Tips for your proposal by DFG](https://www.dfg.de/en/research_funding/proposal_funding_process/individual_grants_programmes/tips_proposals/index.html)

* [Planning and Writing a Grant Proposal: The Basics](https://writing.wisc.edu/handbook/assignments/grants-2) by The Writing Center at University of Wisconsin – Madison 



#### Data management coordination



It makes sense only in big research projects.



* [Data management coordination](https://rdmkit.elixir-europe.org/dm_coordination) in RDMkit



#### Data Management Planning



A Data Management Plan (DMP) describes how research data is handled before the project has commenced, ensuring the traceability of data during the project and beyond. DMPs are often required in a formalized form when submitting a funding application or during the project period, for example with Horizon Europe, ERC grants. The DFG also asks for information on Data Management, although this is not explicitly a DMP and the DFG.  

 

A DMP typically contains the following elements: 



- Data Description/Data Collection

- Documentation and Data Quality

- Storage and Backup

- Legal and Ethical Requirements

- Data Sharing and Archiving

- Data Management, who and what?



**DMPs and Research Funding in Germany**: 

 

DMP requirements differ depending on the funding institution within Germany (see below). 



| Funding Institution                               | DMP Requirements                                                                                                                                                                                                                                                                                                                                                                         | DMP Template       |

|---------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|

| German Research Foundation (DFG)                  | Not explicitly a DMP, but information on data management is usually required in section 2.4 of the application. There are also some subject-specific and program-specific recommendations on how to handle research data in grant applications, but researchers are not obligated to go further than the general guidance provided. See DFG Guidelines on the Handling of Research Data. | Yes (unofficially) |

| Federal Ministry of Education and Research (BMBF) | There are no general requirements with regard to research data. The requirements are defined individually for each tender. See Federal Ministry of Education and Research.                                                                                                                                                                                                               | No                 |

| Volkswagen Foundation                             | Yes, it is a requirement for research funding from the Volkswagen Stiftung that applicants submit a DMP with their application for funding. See Volkswagen Stiftung Open Science Policy.                                                                                                                                                                                                 | Yes                |

| Baden-Württemberg Stiftung                        | No                                                                                                                                                                                                                                                                                                                                                                                       | No                 |

| Fritz Thyssen Foundation                          | No                                                                                                                                                                                                                                                                                                                                                                                       | No                 |

| Hans Böckler Foundation                           | No                                                                                                                                                                                                                                                                                                                                                                                       | No                 |

                                         



Adapted from CESSDA Training Team (2017 – 2022). CESSDA Data Management Expert Guide. Bergen, Norway: CESSDA ERIC. Table by: [CESSDA](https://dmeg.cessda.eu/Data-Management-Expert-Guide/1.-Plan/European-diversity)



And Across Europe: 



| Funding Institution             | DMP Requirements                                                                                                                                                                                                       | DMP Template |

|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|

| European Research Council (ERC) | Yes, applicants must submit a DMP after the first 6 months of the funding period and must continuously update the DMP if significant changes occur, see ERC, Open Science, Section 2. Research Data in Horizon Europe. | Yes          |

| Horizon Europe                  | Yes, applicants must submit a DMP after the first 6 months of the funding period and must continuously update the DMP if significant changes occur, see Horizon Europe.                                                | Yes          |

| European Science Foundation     | No                                                                                                                                                                                                                     | No           |



You may also want to consider the data management standards in your own research field (e.g., Humanities, Social Sciences, Business and Economics) which might inform what you should include in your DMP, in which case, check out the following:



- [Standardised Data Management Plan for Educational Research](https://www.forschungsdaten-bildung.de/stamp-nutzen) of the Research Data Education Network

- Research Data Management in the Social, [Behavioral and Economic Sciences, Chapter 2](https://www.konsortswd.de/aktuelles/publikation/forschungsdatenmanagement-in-den-sozial-verhaltens-und-wirtschaftswissenschaften/)

- [Guidelines for Effective Data Management Plans](https://www.icpsr.umich.edu/web/pages/datamanagement/dmp/index.html) of the Inter-university Consortium for Political and Social Research

- [DMP Wizard](https://www.clarin-d.net/de/aufbereiten/datenmanagementplan-entwickeln) by CLARIN-D, Humanities

- [DMP template](https://www.parthenos-project.eu/portal/dmp) of the EU project PARTHENOS, Humanities



Need some inspiration? You can check out examples of DMPs from successful research applications by checking out the Digital Curation Center (DCC) [here](https://www.dcc.ac.uk/resources/data-management-plans/guidance-examples) and get an idea of what reviewers might be looking for [here](https://scienceeurope.org/media/4brkxxe5/se_rdm_practical_guide_extended_final.pdf).



Applying for funding outside of Germany? You can find out more information about DMP requirements for research funding applications abroad [here](https://dmeg.cessda.eu/Data-Management-Expert-Guide/1.-Plan/European-diversity).



For further information you can also check out the following:



* [RDMO Research Data Management Organiser](https://rdmorganiser.github.io) is funded by DFG

* [DMPonline](https://dmponline.dcc.ac.uk) is created by Digital Curation Centre (DCC), UK

* [Data Stewardship Wizard](https://ds-wizard.org) is created by ELIXIR CZ and NL

* [DMPTool](https://dmptool.org) by CDL, USA

* [Data Management Plan Catalogue](https://libereurope.eu/dmpcatalogue) by the LIBER Research Data Management Working Group

* [Practical Guide to the International Alignment of Research Data Management](https://www.scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management) [[zenodo](https://doi.org/10.5281/zenodo.4915862)]

* [Creating a data management plan (DMP) document](https://help.osf.io/article/144-creating-a-data-management-plan-dmp-document) by OSF



Reviewing and evaluating DMPs:

* [Science Europe Guidelines on the evaluation of DMPs](https://scienceeurope.org/media/22hpslfl/se-rdm-template-5-guidance-on-the-evaluation-of-data-management-plans.docx)

* [Reviewing Data Management Plans](https://www.dcc.ac.uk/events/workshops/reviewing-data-management-plans)



#### Data policies



(Research-) Data Policies are guidelines and recommendations for handling research data. They can be on different levels and from different actors, such as:

* Data policies of third-party funders (e.g. [DFG](https://www.dfg.de/resource/blob/172112/4ea861510ea369157afb499e96fb359a/leitlinien-forschungsdaten-data.pdf) or [ERC](https://ec.europa.eu/research/participants/data/ref/h2020/other/hi/oa-pilot/h2020-hi-erc-oa-guide_en.pdf))

* Data policies of publishers and/or journals (e.g. [American Economic Association](https://www.aeaweb.org/journals/data/data-code-policy), [Oxford University Press](https://academic.oup.com/pages/open-research/research-data), other policies can be found at [ReplicationWiki](http://replication.uni-goettingen.de/wiki/index.php/Journal_publication_policies#cite_note-1))

* Discipline-specific guidelines (e.g. [DFG](https://www.dfg.de/de/grundlagen-rahmenbedingungen/grundlagen-und-prinzipien-der-foerderung/forschungsdaten/empfehlungen))

* Guidelines of research institutions (e.g. [institute-specific](https://www.forschungsdaten.org/index.php/Data_Policies#Institutionelle_Policies) and [project-specific](https://www.forschungsdaten.org/index.php/Data_Policies#Forschungsdaten-Policies_f%C3%BCr_Forschungsprojekte))



There are also tips, toolkits and other materials to help institutions and projects develop a data policy. For example, you can find materials from these projects:

* [FDMentor](https://www.forschungsdaten.org/index.php/FDMentor): [Empfehlungen zur Erstellung institutioneller Forschungsdaten-Policies](https://zenodo.org/records/3333410), [Strategischer Leitfaden zur Etablierung einer institutionellen Forschungsdaten-Policy](https://zenodo.org/records/3333392), [RISE-DE](https://zenodo.org/records/3585556)

* [LEARN](https://learn-rdm.eu/en/about/): [Toolkit of Best Practice for Research Data Management](https://learn-rdm.eu/en/dissemination/toolkit/)



#### Reusing data



* [Existing data](https://rdmkit.elixir-europe.org/existing_data) in RDMkit

* [Reusing existing data](https://www.ugent.be/en/research/datamanagement/before-research/data-reuse.htm) by Ghent University



First, find it.

* Search in a suitable data repository which you can find in [Registries](#registries).

* Search at

    * [DataCite](https://search.datacite.org)

    * [OpenAIRE](https://explore.openaire.eu/search/find)

    * [Google Dataset Search](https://datasetsearch.research.google.com)

* Check the links in data papers:

    * list of data journals at [Forschungsdaten.org](https://www.forschungsdaten.org/index.php/Data_Journals)

    * list of data journals at [GitHub repo data-journals](https://github.com/MaxiKi/data-journals/blob/main/data_journals_characteristics.csv)

    * list of data journals at [Wikidata](https://w.wiki/6$vi)

    * examples of data journals:

          1. [Data](https://www.mdpi.com/journal/data) by MDPI

          2. [Scientific Data](https://www.nature.com/sdata) by Nature

          3. [Data in brief](https://www.sciencedirect.com/journal/data-in-brief) by ScienceDirect



Check quality of the data. Check licenses. If you reuse data, cite it.



### Executing a project



#### Collecting data



The focus here on:

* Reusing existing data

* Collecting new data



General info on collecting data

* [Collecting data](https://rdmkit.elixir-europe.org/collecting) in RDMkit



Lists of data sources:

* [10 Great Places to Find Free Datasets for Your Next Project](https://careerfoundry.com/en/blog/data-analytics/where-to-find-free-datasets)

* [21 Places to Find Free Datasets for Data Science Projects](https://careers.uw.edu/blog/2021/10/05/21-places-to-find-free-datasets-for-data-science-projects-shared-article-from-dataquest)



Registries of data repositories:

* [FAIRsharing](https://fairsharing.org) is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies

* [re3data](https://www.re3data.org) is a registry of research data repositories



Metadata and data portals:

* [Kaggle](https://www.kaggle.com/datasets)

* [Google Dataset Search](https://datasetsearch.research.google.com)

* [The official portal for European data](https://data.europa.eu)



Methods of collecting data:

* [7 Data Collection Methods in Business Analytics](https://online.hbs.edu/blog/post/data-collection-methods) by Harvard Business School



#### Creating metadata



* [User guide on creating metadata](https://guides.library.stanford.edu/research-metadata) by Stanford University Libraries offers advice on gathering basic and semantic metadata for research data, including a list of some metadata standards, ontologies, and [metadata creation tools](https://guides.library.stanford.edu/research-metadata/metadata-tools).

  

#### Organizing data



* [Data organization](https://rdmkit.elixir-europe.org/data_organisation) in RDMkit

* [Folder structure, file names, and versioning](https://snd.gu.se/en/manage-data/organise/folder-structure-filenames-versioning) by Swedish National Data Service

* [File Naming and Versioning](https://researchdata.wisc.edu/file-naming-and-versioning)

* [Naming files and folders](https://www.imperial.ac.uk/research-and-innovation/support-for-staff/scholarly-communication/research-data-management/organising-and-describing-data/naming-files-and-folders) by Imperial College London

* [File Naming Conventions & Version Control](https://records.princeton.edu/records-management-manual/file-naming-conventions-version-control)

* [File Naming Conventions: simple rules save time and effort](https://www.abdn.ac.uk/staffnet/documents/policy-zone-information-policies/File%20Naming%20Conventions%20July%202017.pdf)



#### Data storage



Separate storage for sensitive data



* [Data Storage & Backup](https://libguides.libraries.wsu.edu/rdmlibguide/datastorage)

* [5 Research Data Storage Problems (and Tips) in Research Data Management](https://www.eudat.eu/news/5-research-data-storage-problems-and-tips-in-research-data-management)



#### Data backup



* [Data backups 101: A complete guide for 2023](https://us.norton.com/blog/how-to/data-backup)

* [Data Backup Strategies for Your PhD/Research Data](https://www.researchvoyage.com/data-backup-research-data)

* [Backup, Storage & Security](https://guides.library.pdx.edu/c.php?g=318088&p=2185650)



#### Cleaning data



* [The Ultimate Guide to Data Cleaning](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4) by Omar Elgabry

* [What Is Data Cleaning and Why Does It Matter?](https://careerfoundry.com/en/blog/data-analytics/what-is-data-cleaning) by Will Hillier

* [Data cleaning tutorial at Kaggle](https://www.kaggle.com/learn/data-cleaning)

* [Top ten ways to clean your data from Microsoft](https://support.microsoft.com/en-au/office/top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19)



#### Data exploration



* [15 Data Exploration techniques to go from Data to Insights](https://towardsdatascience.com/15-data-exploration-techniques-to-go-from-data-to-insights-93f66e6805df)

* [Comprehensive data exploration with Python](https://www.kaggle.com/code/pmarcelino/comprehensive-data-exploration-with-python)

* [11 Open Source Data Exploration Tools You Need to Know in 2023](https://opendatascience.com/11-open-source-data-exploration-tools-you-need-to-know-in-2023)

* [Data Exploration in R (9 Examples) | Exploratory Analysis & Visualization](https://statisticsglobe.com/data-exploration-r)



#### Data interpretation

* [Data Interpretation](https://unacademy.com/content/ssc/study-material/mathematics/data-interpretation/) by Unacademy

* [Data Interpretation: Method, Types, Tips with Solved Examples](https://testbook.com/maths/data-interpretation) by Testbook

* [Basic data interpretation](https://www.port.ac.uk/student-life/help-and-advice/study-skills/written-assignments/basic-data-interpretation) by University of Portsmouth



#### Anonymising data



* [Data anonymization](https://en.wikipedia.org/wiki/Data_anonymization) at Wikipedia

* [Anonymisation and Pseudonymisation](https://www.ucl.ac.uk/data-protection/guidance-staff-students-and-researchers/practical-data-protection-guidance-notices/anonymisation-and) at University College London

* [Anonymising quantitative data](https://ukdataservice.ac.uk/learning-hub/research-data-management/anonymisation/anonymising-quantitative-data) by UK Data Service

* [Anonymisation and pseudonymisation](https://www.dataprotection.ie/en/dpc-guidance/anonymisation-pseudonymisation) at dataprotection.ie



Topics:

* Data Masking

* Pseudonimisation

* Aggregation

* Derived Data



#### Data protection



* [Data Protection Notice for Research Funding from the German Research Foundation (DFG)](https://www.dfg.de/en/service/privacy_policy/research_funding/index.html)

* [Germany - Data Protection Overview](https://www.dataguidance.com/notes/germany-data-protection-overview)

* [Data protection in research](https://www.helmholtz.de/en/about-us/our-values/data-protection) by Helmholtz.de



#### Data provenance



* [Data provenance](https://rdmkit.elixir-europe.org/data_provenance) in RDMkit



#### Legal aspects



* [FAIRmat Guide to Legal Aspects in Research Data Management](https://doi.org/10.5281/zenodo.11083303)

* [Research Data Management - Legal and Practical Aspects](https://doi.org/10.5281/zenodo.6841070) by Anja Perry, & Jan-Ocko Heuer (2022, Juli 15)

* [Research Data Management - Legal Aspects](https://doi.org/10.5281/zenodo.3349706) by Maurice Schleußinger (2019, Juli 24)



### Finishing a project



The [difference between sharing, publishing & archiving](https://datacarpentry.org/rr-publication/01-publication) is:

* sharing: any way of sharing information, could mean also emailing. It means also making research data available throughout the research lifecycle, especially during the active research phase, typically via cloud storage.

* publishing: citable artifact, discoverable.

* archiving: long-term preservation.



#### Sharing data



There are many benefits to sharing data.



You can share the data via GitHub



#### Publishing data



Data journals:

* list of data journals at [Forschungsdaten.org](https://www.forschungsdaten.org/index.php/Data_Journals)

* list of data journals at [GitHub repo data-journals](https://github.com/MaxiKi/data-journals/blob/main/data_journals_characteristics.csv)

* list of data journals at [Wikidata](https://w.wiki/6$vi)



#### Presenting data



"from infographics to narrative reports, case studies and long form investigative articles, to graffiti or conceptual art"



#### Data licensing



* [License chooser](https://creativecommons.org/choose)

* [RDA & CODATA Legal Interoperability Of Research Data: Principles And Implementation Guidelines](https://www.rd-alliance.org/rda-codata-legal-interoperability-research-data-principles-and-implementation-guidelines-now)

* [How do I license my research data? OpenAIRE](https://www.openaire.eu/how-do-i-license-my-research-data)

* [What is the most appropriate license for my data?](https://help.figshare.com/article/what-is-the-most-appropriate-licence-for-my-data)

* [The Legal Side of Open Source](https://opensource.guide/legal)



For restricted access data:

* [Restrictive Licence Template](https://library.unimelb.edu.au/Digital-Scholarship/restrictive-licence-template)

* [Data Availability Statements for Restricted Data](https://social-science-data-editors.github.io/guidance/DCAS_Restricted_data.html)

* [Aligning restricted access data with FAIR: a systematic review](https://doi.org/10.7717%2Fpeerj-cs.1038)



#### Archiving data



* [Research data archiving](https://en.wikipedia.org/wiki/Research_data_archiving) at Wikipedia

* [On the Long-term Archiving of Research Data](https://doi.org/10.1007/s12021-023-09621-x)



## RDM for organizations



* [LEARN Project resources](https://learn-rdm.eu/en/about) are resources to help Research Performing Institutions manage their research data



### How to develop RDM services



* [Engaging Researchers with Data Management: The Cookbook](https://library.oapen.org/handle/20.500.12657/24568) [[pdf](https://library.oapen.org/bitstream/handle/20.500.12657/24568/9781783747993.pdf?sequence=1&isAllowed=y)]

* [How to Develop RDM Services - a guide for HEIs](https://www.dcc.ac.uk/guidance/how-guides/how-develop-rdm-services)

* [The Realities of Research Data Management. Part Four: Sourcing and Scaling University RDM Services](https://www.oclc.org/content/dam/research/publications/2018/oclcresearch-rdm-part-four-sourcing-scaling.pdf)

* [Ten simple rules for starting FAIR discussions in your community](https://doi.org/10.1371/journal.pcbi.1011668)



### How to choose an RDM repository



* [Evaluation of data repositories based on the FAIR Principles for IDCC 2017 practice paper](https://doi.org/10.4121/uuid:5146dd06-98e4-426c-9ae5-dc8fa65c549f)



### Persistent Identifiers



* [DOI registration agencies](https://www.doi.org/registration_agencies.html) is a list of current DOI registration agencies

* [URN](https://www.iana.org/assignments/urn-namespaces/urn-namespaces.xml) is a list of all registered namespaces provided by the Internet Assigned Numbers Authority (IANA)



## Discipline-specific RDM



#### Social and economic data



* [Auffinden-Zitieren-Dokumentieren](https://auffinden-zitieren-dokumentieren.de) by ZBW, GESIS and RatSWD



#### Digital Humanities



* [Ending principles for digital humanities projects](https://endings.uvic.ca/principles.html)

* [Paper: Digitale Werkzeuge zur textbasierten Annotation, Korpusanalyse und Netzwerkanalyse in den Geisteswissenschaften](https://tuprints.ulb.tu-darmstadt.de/17850/1/Digital_Philology__Working_Papers_in_Digital_Philology_vol002.pdf) that sums up and explains different tools

* [The Programming Historian](https://programminghistorian.org/) provides tutorials about DH topics for humanists 



## Discipline-specific tools



### Digital Humanities and Social Sciences



* [OpenMethods.Dariah](https://openmethods.dariah.eu) is a list of digital humanities tools and methods

* [CLARIN-D](https://www.clarin-d.net/de/) is a is a research infrastructure that helps researchers of Humanities, Cultural and Social Sciences with accessing, preparing and analysing of research data

* [TAPoR](https://tapor.ca/tools) is a list of research tools for text analysis

* [SSH Open Market Place](https://marketplace.sshopencloud.eu/) is a place for resources for research in Social Sciences and Humanities

* [BAS](https://clarin.phonetik.uni-muenchen.de/BASWebServices/interface) is a set of tools for speech sciences and technology

* [TextGrid](https://textgrid.de/en/) is a virtual research environment for the humanities that is optimised for working with TEI-coded resources

* [Awesome Digital Humanities](https://dh-tech.github.io/awesome-digital-humanities/) is a curated list of tools, resources, and services supporting the Digital Humanities

* [DigEdTnT: Digital Edition Creation Pipelines: Tools and Transitions](https://digedtnt.github.io/) provides tool and workflow descriptions, workshops and online seminars on tools for annotating and publishing digital editions in the TEI format. The web pages include information on [FromThePage](https://digedtnt.github.io/fromthepage/), [Transkribus](https://digedtnt.github.io/transkribus/), [ediarum](https://digedtnt.github.io/ediarum/), [OpenRefine](https://digedtnt.github.io/openrefine/), [FairCopy](https://digedtnt.github.io/faircopy/), [ba[sic]](https://digedtnt.github.io/basic/), [TEI Publisher](https://digedtnt.github.io/teipublisher/) and [ediarum.Web](https://digedtnt.github.io/ediarumWEB/).



## Discipline-specific repositories



### Digital editions



* [Patrick Sahle's Catalog of Digital Scholarly Editions](https://v3.digitale-edition.de)

* [RIDE – A review journal for digital editions and resources](https://ride.i-d-e.de)

* [TEI Publisher](https://teipublisher.com) is a software for publishing digital editions

* [ediarum](https://www.ediarum.org) is a software for creating and publishing digital editions

* [KONDE - Kompetenzzentrum Digitale Edition](https://www.digitale-edition.at/context:konde) is a guideline to publish a digital edition

* [Dig-Ed-Cat](https://dig-ed-cat.acdh.oeaw.ac.at/) is a Catalogue of Digital Editions



### Domain-specific NFDI consortia



There are 26 domain-specific [NFDI](https://www.nfdi.de) consortia aiming to ensure FAIR data in Germany.



#### NFDI consortia in Humanities and Social Sciences



* [BERD@NFDI](https://www.berd-nfdi.de): NFDI for Business, Economic and Related Data

* [KonsortSWD](https://www.konsortswd.de/en/): Consortium for the Social, Educational, Behavioural and Economic Sciences

* [NFDI4Culture](https://nfdi4culture.de): Consortium for Research Data on Material and Immaterial Cultural Heritage

* [NFDI4Memory](https://4memory.de): The Consortium for the Historically Oriented Humanities

* [NFDI4Objects](https://www.nfdi4objects.net): Research Data Infrastructure for the Material Remains of Human History

* [Text+](https://www.text-plus.org): Language and text-based research data infrastructure



#### NFDI consortia in Engineering Sciences



* [NFDI4DataScience](https://www.nfdi4datascience.de): NFDI for Data Science and Artificial Intelligence

* [NFDI4Energy](https://nfdi4energy.uol.de): National Research Data Infrastructure for Interdisciplinary Energy System Research

* [NFDI4Ing](https://nfdi4ing.de): NFDI for Engineering Sciences

* [NFDI-MatWerk](https://nfdi-matwerk.de): National Research Data Infrastructure for Materials Science and Materials Engineering

* [NFDIxCS](https://nfdixcs.org): National Research Data Infrastructure for and with Computer Science



#### NFDI consortia in Life Sciences



* [DataPLANT](https://www.nfdi4plants.de): Plant research data

* [FAIRagro](https://www.fairagro.net): FAIR Data Infrastructure for Agrosystems

* [NFDI4Immuno](https://www.nfdi4immuno.de): National Research Data Infrastructure for Immunology

* [GHGA](https://www.ghga.de): National Research Data Infrastructure for Immunologyv

* [NFDI4Biodiversity](https://www.nfdi4biodiversity.org): Biodiversity, Ecology and Environmental Data

* [NFDI4BIOIMAGE](https://nfdi4bioimage.de): National research data infrastructure for microscopy and bioimage analysis

* [NFDI4Health](https://www.nfdi4health.de): NFDI personal health data

* [NFDI4Microbiota](https://nfdi4microbiota.de): NFDI for Microbiota Research



#### NFDI consortia in Natural Sciences



* [DAPHNE4NFDI](https://www.daphne4nfdi.de): Data from PHoton and Neutron Experiments for NFDI

* [FAIRmat](https://www.fairmat-nfdi.eu): FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids

* [NFDI4Cat](https://nfdi4cat.org): NFDI for sciences related to catalysis

* [MaRDI](https://www.mardi4nfdi.de): Mathematical Research Data Initiative

* [NFDI4Chem](https://www.nfdi4chem.de): Chemistry consortium for the NFDI

* [NFDI4Earth](https://www.nfdi4earth.de): NFDI Consortium Earth System Sciences

* [PUNCH4NFDI](https://www.punch4nfdi.de): Particles, Universe, NuClei and Hadrons for the NFDI