Data and variable key for Dunham, Dotsch, Clark, & Stepanova, "The development of White-Asian categorization: Contributions from skin color and other physiognomic cues"

Identifiers for space-related data from studies that investigate biological and health responses of terrestrial life to spaceflight

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

This dataset aggregates information about 191 research data repositories that were shut down. The data collection was based on the registry of research data repositories re3data and a comprehensive content analysis of repository websites and related materials. Documented in the dataset are the period in which a repository was active, the risks resulting in its shutdown, and the repositories taking over custody of the data after.

These research datasets are the updated version of the conference poster "Research data repositories and their metadata: A comparative study," presented by Ms. Kavya Asok and Ms. Snigdha Dandpat in a Conference on Open and FAIR Data Ecosystem: Principles, Policies, and Platforms scheduled from 11th -13th September 2023, at IIC, New Delhi. The study describes the features of a select number of RDRs and analyzes their metadata practices.

The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.

Research Data Repositories Market Outlook

According to our latest research, the global research data repositories market size reached USD 4.12 billion in 2024, driven by the surging demand for secure, accessible, and scalable data management solutions across academic, government, and corporate sectors. The market is projected to expand at a robust CAGR of 8.7% from 2025 to 2033, reaching a forecasted value of USD 8.65 billion by 2033. This impressive growth trajectory is primarily attributed to the increasing emphasis on open science, data transparency, and regulatory compliance, which are compelling organizations to invest in advanced research data repository solutions.

One of the primary growth factors driving the research data repositories market is the global shift towards open data policies and mandates by funding agencies and governments. The proliferation of open-access initiatives, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, has significantly increased the need for robust data repositories that can support data sharing, reproducibility, and long-term preservation. As research outputs become more data-intensive and collaborative, the ability to store, manage, and disseminate large datasets efficiently has become a strategic imperative for research institutions and organizations worldwide. This trend is further reinforced by the growing recognition of data as a critical asset in scientific discovery, innovation, and policy-making.

Another major driver is the rapid digital transformation occurring across academia, government, and the corporate sector. Organizations are increasingly leveraging cloud-based research data repositories to overcome traditional storage limitations, enhance data security, and streamline workflows. The adoption of advanced technologies such as artificial intelligence, machine learning, and blockchain within these repositories is also enhancing data curation, metadata management, and access control. This technological evolution is enabling researchers and organizations to extract greater value from their data assets while ensuring compliance with evolving data governance standards and privacy regulations, such as GDPR and HIPAA.

The expansion of interdisciplinary and international research collaborations is also fueling the demand for scalable and interoperable research data repositories. As research projects become more complex and involve multiple stakeholders across different geographies, there is a growing need for standardized platforms that facilitate seamless data exchange and integration. This is particularly evident in domains such as health sciences, environmental research, and social sciences, where data sharing and cross-institutional collaboration are essential for addressing global challenges. Furthermore, the increasing availability of funding for research infrastructure development, particularly in emerging economies, is creating new opportunities for market growth.

From a regional perspective, North America currently dominates the research data repositories market, owing to its advanced research ecosystem, strong government support, and the presence of leading technology providers. Europe follows closely, driven by stringent data protection regulations and a vibrant academic landscape. The Asia Pacific region is expected to witness the fastest growth over the forecast period, supported by significant investments in research infrastructure, rising adoption of digital technologies, and increasing participation in global research initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as governments and institutions in these regions ramp up their efforts to enhance research capacity and data management capabilities.

Type Analysis

The research data repositories market is segmented by type into institutional repositories, disciplinary repositories, generalist repositories, and others. Institutional repositories form the backbone of most academic and research organizations, serving as centralized platforms for storing, managing, and disseminating research outputs generated by faculty, students, and staff. These repositories are increasingly being adopted as part of open access and research data management policies, enabling institutions to showcase their research impact, comply with funder mandates, and facilitate knowledge sharing. The growing emphasis o

Research Data Repositories Market Outlook

According to our latest research, the global research data repositories market size reached USD 3.1 billion in 2024, reflecting a robust expansion fueled by the rising demand for data-driven research and open science initiatives. The market is anticipated to grow at a CAGR of 10.2% from 2025 to 2033, with the total market forecasted to reach USD 7.4 billion by 2033. Key growth factors include the proliferation of digital research outputs, increasing mandates for data sharing by funding agencies, and the rapid evolution of cloud-based repository solutions.

One of the primary growth drivers for the research data repositories market is the accelerating adoption of open science policies by governments, research councils, and academic institutions worldwide. These policies mandate that research data be made openly accessible, reusable, and interoperable, which has led to a surge in the establishment and utilization of repositories. Additionally, the exponential growth in research output, especially in fields such as genomics, climate science, and social sciences, necessitates efficient data management and sharing platforms. As the volume, variety, and velocity of research data increase, organizations are investing in sophisticated repository solutions to ensure data integrity, discoverability, and long-term preservation, further propelling the market’s expansion.

Technological advancements have also played a pivotal role in shaping the research data repositories market. The integration of artificial intelligence, machine learning, and advanced metadata management tools within repository platforms has significantly enhanced data curation, searchability, and security. These innovations are helping institutions and researchers manage large and complex datasets more effectively, driving adoption across diverse end-user segments. Moreover, the shift towards cloud-based deployment models has enabled scalable, cost-effective, and collaborative environments for data storage and sharing, making research data repositories more accessible to a broader range of organizations, including those with limited IT infrastructure.

Another critical factor fueling market growth is the increasing emphasis on research reproducibility and transparency. Funding agencies and scientific publishers are increasingly requiring researchers to deposit their data in trusted repositories as a prerequisite for grant approval or publication. This trend is particularly prominent in regions such as North America and Europe, where regulatory frameworks and research assessment policies are more mature. The growing recognition of data as a valuable research output, alongside publications, is encouraging institutions to invest in robust repository infrastructure, driving sustained market growth across the globe.

Regionally, North America continues to dominate the research data repositories market, accounting for the largest revenue share in 2024, followed closely by Europe. These regions benefit from well-established research ecosystems, high digital literacy, and strong policy support for open data initiatives. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, driven by substantial investments in research infrastructure, increasing international collaborations, and government-led digitalization programs. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a lower base, as academic and governmental institutions in these regions gradually embrace open data practices and invest in repository solutions.

Type Analysis

The research data repositories market is segmented by type into institutional repositories, subject-based repositories, general-purpose repositories, and others. Institutional repositories are primarily managed by universities, research institutes, and academic organizations to store and disseminate the scholarly output of their members. These repositories have gained significant traction due

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as:
von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Further information is given in the corresponding data paper:
von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

Science Data Bank - An Open and General-Purpose Data Repository

Data sharing for submitted manuscript. Full citation information will be shared upon publication.Title: Open but Hidden: Open Access gaps in the National Science Foundation funded publications as posted online to the NSF Public Access Repository.AbstractObjectives: In August of 2022, the U.S. federal government released new guidelines for making publicly funded research open and available. This study looks at the availability of National Science Foundation (NSF) funded research within the designated Public Access Repository (PAR) from two research intensive (R1) universities as required under the previous 2016 policy to evaluate the current state of compliance before new guidelines go into effect.Methods: The project team searched the NSF PAR for records published between 2017 and 2021 from two institutions. Records were reviewed to determine if the PAR held a deposited copy or provided a link out to the publisher held version(s). Where only a publisher linkout was provided, links were evaluated for the availability of an open access version.Results: A total of 841 unique records were identified. Of these 42% had a deposited PDF version as required by the NSF 2016 Public Access Policy. The remaining 58% relied exclusively on a publisher-held version. However, 45% of the provided publisher links directed to paywall versions. Additionally, 24% of records required users to have specialized knowledge of the CHORUS initiative in order to navigate from the initial paywall prompt to a publicly available version.Conclusions: Despite having a public access mandate since 2016, NSF compliance rates remain low. It seems unlikely that the additional guidelines introduced under the 2022 memo, meant to further drive public access to federal research, will increase compliance without additional dedication to oversight and/or imposed consequences for non-compliance.

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and their corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, but this increased with the publication date. There was a..., Article and dataset fairness To assess the utility of open articles and their datasets as an educational tool in an undergraduate academic setting, I measured the congruence of each pair to a set of best practices and guiding principles. I assessed ten guiding principles and best practices (Table 1), where each category was scored ‘1’ or ‘0’ based on whether it met that criteria, with a total possible score of ten. Open grading policies Students were allowed to specify the percentage weight for each assessment category in the course, including 1) six coding exercises (Exercises), 2) one lead exercise (Lead Exercise), 3) fourteen annotation assignments of readings (Annotations), 4) one final project (Final Project), 5) five discussion board posts and a statement of learning reflection (Discussion), and 6) attendance and participation (Participation). I examined if assessment categories (independent variable) were weighted (dependent variable) differently by students using an analysis of ..., , # Data for: Integrating open education practices with data analysis of open science in an undergraduate course

Author: Marja H Bakermans Affiliation: Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609 USA ORCID: https://orcid.org/0000-0002-4879-7771 Institutional IRB approval: IRB-24–0314

Data and file overview

The full dataset file called OEPandOSdata (.xlsx extension) contains 8 files. Below are descriptions of the name and contents of each file. NA = not applicable or no data available

1. BestPracticesData.csv
   • Description: Data to assess the adherence of articles and datasets to open science best practices.
   • Column headers and descriptions:
     • Article: articles used in the study, numbered randomly
     • F1: Findable, Data are assigned a unique and persistent doi
     • F2: Findable, Metadata includes an identifier of data
     • F3: Findable, Data are registered in a searchable database
     • A1: ...

Identifiers for hardware used in experiments that investigate biological and health responses of terrestrial life to spaceflight

The purpose of the national research data repository HARDMIN (Hellenic Academic Research Data Management Initiative) is to collect all research data generated by Greek researchers and academics. The repository aims to address the critical need for the secure storage and publication of research data from the Greek scientific community, to increase transparency in research, to enable reuse by interested researchers worldwide, to accelerate the digital transformation of the research field in our country, and to adopt competitive practices in research proposals and scientific communication. All researchers from Greek Universities are connected to the repository with their credentials and can easily upload their research data. Special teams of editors, either within an academic unit (e.g., laboratory head) or at an institutional level (e.g., Library staff), can make your data public, which will have permanent identifiers, the ability to link to your unique ORCiD identifier, and coupling with your published work, e.g., with a scientific journal article. For special cases, access can be controlled and provided upon request. HARDMIN has been developed with the open-source software CKAN and, together with HELIX, constitutes the national digital scientific infrastructure (eInfrastructure) software for providing catalog and repository services for scientific data, part of the infrastructure network for Open Science. The repository will have the ability to connect to existing repositories and retrieve the corresponding data from already existing collections. The repository is accessible at https://hardmin.heal-link.gr, and interested researchers can contact their local Library for more details. Currently, the repository is operating on a pilot basis to resolve technical details. Translated from Greek Original Text: Σκοπός της λειτουργίας του εθνικού αποθετηρίου ερευνητικών δεδομένων HARDMIN (Hellenic Academic Research Data Management Initiative)είναι η συγκέντρωση του συνόλου των ερευνητικών δεδομένων που δημιουργούνται από Έλληνες ερευνητές και ακαδημαϊκούς. Το αποθετήριο έρχεται να καλύψει την καίρια ανάγκη ασφαλούς φύλαξης και δημοσίευσης ερευνητικών δεδομένων της ελληνικής επιστημονικής κοινότητας για την αύξηση της διαφάνειας στην έρευνα, τη δυνατότητα επαναχρησιμοποίησης από τους ενδιαφερόμενους ερευνητές ανά τον κόσμο, της επιτάχυνσής του ψηφιακού μετασχηματισμού του ερευνητικού πεδίου στη χώρα μας και την υιοθέτηση ανταγωνιστικών πρακτικών στον στίβο των ερευνητικών προτάσεων και της επιστημονικής επικοινώνησης. Στο αποθετήριο συνδέονται όλοι οι ερευνητές των ελληνικών Πανεπιστημίων με τα διαπιστευτήριά τους και μπορούν να αναρτήσουν με ευκολία τα ερευνητικά τους δεδομένα. Ειδικές ομάδες συντακτών, είτε εντός μιας ακαδημαϊκής μονάδας (π.χ. υπεύθυνος εργαστηρίου), είτε σε ι δρυματικό επίπεδο (π.χ. προσωπικό Βιβλιοθήκης), μπορούν να καταστήσουν δημόσια τα δεδομένα σας, τα οποία θα διαθέτουν μόνιμα αναγνωριστικά, δυνατότητες διασύνδεσης με το μοναδικό σας αναγνωριστικό ORCiD και σύζευξης με το δημοσιευμένο σας έργο, π.χ. με ένα άρθρο επιστημονικού περιοδικού. Για ειδικές περιπτώσεις, η πρόσβαση μπορεί να είναι ελεγχόμενη και να παρέχεται κατόπιν αιτήματος. Το HARDMIN έχει αναπτυχθεί με το ανοικτό λογισμικό CKAN και αποτελεί, μαζί με το HELIX την εθνική ψηφιακή επιστημονική υποδομή (eInfrastructure) λογισμικού για την παροχή υπηρεσιών καταλόγου και αποθετηρίου επιστημονικών δεδομένων, μέρος του πλέγματος υποδομών για την Ανοικτή Επιστήμη. Το αποθετήριο θα διαθέτει τη δυνατότητα σύνδεσης με τα υφιστάμενα αποθετήρια και άντλησης των αντίστοιχων δεδομένων από ήδη υπάρχουσες συλλογές. Το αποθετήριο είναι προσβάσιμο από τη διεύθυνση https://hardmin.heal-link.gr, ενώ οι ενδιαφερόμενοι ερευνητές μπορούν να επικοινωνούν με την οικεία Βιβλιοθήκη τους για περισσότερες λεπτομέρειες. Αυτή τη στιγμή, το αποθετήριο λειτουργεί πιλοτικά για τη διευθέτηση τεχνικών λεπτομερειών.

This survey aims to collect information about the habits and needs of Ca’ Foscari University researchers regarding the management of research data in order to improve services related to Open Science and in particular to the Unive Datarepository, the institutional archive for research data, adopted by the University in July 2024.
Here you can consult the format of the questionnaire and the reports on the survey in pdf format.
Library System ran the survey between December 2024 and February 2025. In total, 161 responses were collected and are published here: the raw results are provided in CSV format.

Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In Asia, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in Asian countries. It includes detailed records from different registry sources and repository platforms.You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at https://doi.org/10.5281/zenodo.12566244

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as: von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487

Further information is given in the corresponding data paper: von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch