western blot,

Spreadsheet listing data repositories that are recommended by Scientific Data (Springer Nature) as being suitable for hosting data associated with peer-reviewed articles. Please see the repository list on Scientific Data's website for the most up to date list.

This dataset contains article metadata and information about Open Science Indicators for approximately 139,000 research articles published in PLOS journals from 1 January 2018 to 30 March 2025 and a set of approximately 28,000 comparator articles published in non-PLOS journals. This is the tenth release of this dataset, which will be updated with new versions on an annual basis.This version of the Open Science Indicators dataset shares the indicators seen in the previous versions as well as fully operationalised protocols and study registration indicators, which were previously only shared in preliminary forms. The v10 dataset focuses on detection of five Open Science practices by analysing the XML of published research articles:Sharing of research data, in particular data shared in data repositoriesSharing of codePosting of preprintsSharing of protocolsSharing of study registrationsThe dataset provides data and code generation and sharing rates, the location of shared data and code (whether in Supporting Information or in an online repository). It also provides preprint, protocol and study registration sharing rates as well as details of the shared output, such as publication date, URL/DOI/Registration Identifier and platform used. Additional data fields are also provided for each article analysed. This release has been run using an updated preprint detection method (see OSI-Methods-Statement_v10_Jul25.pdf for details). Further information on the methods used to collect and analyse the data can be found in Documentation.Further information on the principles and requirements for developing Open Science Indicators is available in https://doi.org/10.6084/m9.figshare.21640889.Data folders/filesData Files folderThis folder contains the main OSI dataset files PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv, which containdescriptive metadata, e.g. article title, publication data, author countries, is taken from the article .xml filesadditional information around the Open Science Indicators derived algorithmicallyand the OSI-Summary-statistics_v10_Jul25.xlsx file contains the summary data for both PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv.Documentation folderThis file contains documentation related to the main data files. The file OSI-Methods-Statement_v10_Jul25.pdf describes the methods underlying the data collection and analysis. OSI-Column-Descriptions_v10_Jul25.pdf describes the fields used in PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv. OSI-Repository-List_v1_Dec22.xlsx lists the repositories and their characteristics used to identify specific repositories in the PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv repository fields.The folder also contains documentation originally shared alongside the preliminary versions of the protocols and study registration indicators in order to give fuller details of their detection methods.Contact details for further information:Iain Hrynaszkiewicz, Director, Open Research Solutions, PLOS, ihrynaszkiewicz@plos.org / plos@plos.orgLauren Cadwallader, Open Research Manager, PLOS, lcadwallader@plos.org / plos@plos.orgAcknowledgements:Thanks to Allegra Pearce, Tim Vines, Asura Enkhbayar, Scott Kerr and parth sarin of DataSeer for contributing to data acquisition and supporting information.

Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.

These are the slides of a presentation given at figshare Fest New Zealand on 27th October 2017 at Auckland University. One slide has been removed for copyright reasons from the original slide deck.

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)

We introduce the FunAndes database, a compilation of functional trait data for the Andean flora spanning six countries. FunAndes contains data on 24 traits across 2,694 taxa, for a total of 105,466 entries. The database features plant-morphological attributes including growth form, and leaf, stem, and wood traits measured at the species or individual level, together with geographic metadata (i.e., coordinates and elevation). FunAndes follows the field names, trait descriptions and units of measurement of the TRY database. It is currently available in open access in the FIGSHARE data repository, and will be part of TRY’s next release. Open access trait data from Andean plants will contribute to ecological research in the region, the most species rich terrestrial biodiversity hotspot.

A conference poster by Monash University Library for Open Repositories 2017, outlining the transition of the Monash University Research Repository from one platform to three modern expandable solutions that meet the needs of the University's various collections and research community.

Bridges is Monash University's repository for research data, collections, non-traditional research outputs and research activities.

This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.

Repository for all data, figures, theses, publications, posters, presentations, filesets, videos, datasets, negative data in a citable, shareable and discoverable manner with Digital Object Identifiers. Allows to upload any file format to be made visualisable in the browser so that figures, datasets, media, papers, posters, presentations and filesets can be disseminated in a way that the current scholarly publishing model does not allow. Features integration with ORCID, Symplectic Elements, can import items from Github and is a source tracked by Altmetric.com. Figshare gives users unlimited public space and 1GB of private storage space for free. Data are digitally preserved by CLOCKSS. Supported by Digital Science, a division of Macmillan Publishers Limited, as a community-based, open science project that retains its autonomy.

These data were generated for an investigation of research data repository (RDR) mentions in biuomedical research articles.

Supplementary Table 1 is a discrete subset of SciCrunch RDRs used to study RDR mentions in biomedical literature. We generated this list by starting with the top 1000 entries in the SciCrunch database, measured by citations, removed entries for organizations (such as universities without a corresponding RDR) or non-relevant tools (such as reference managers), updated links, and consolidated duplicates resulting from RDR mergers and name variations. The resulting list of 737 RDRs is shown in with as a base based on a source list of RDRs in the SciCrunch database. The file includes the Research Resource Identifier (RRID), the RDR name, and a link to the RDR record in the SciCrunch database.

Supplementary Table 2 shows the RDRs, associated journals, and article-mention pairs (records) with text snippets extracted from mined Methods text in 2020 PubMed articles. The dataset has 4 components. The first shows the list of repositories with RDR mentions, and includes the Research Resource Identifier (RRID), the RDR name, the number of articles that mention the RDR, and a link to the record in the SciCrunch database. The second shows the list of journals in the study set with at least 1 RDR mention, andincludes the Journal ID, nam, ESSN/ISSN, the total count of publications in 2020, the number of articles that had text available to mine, the number of article-mention pairs (records), number of articles with RDR mentions, the number of unique RDRs mentioned, % of articles with minable text. The third shows the top 200 journals by RDR mention, normalized by the proportion of articles with available text to mine, with the same metadata as the second table. The fourth shows text snippets for each RDR mention, and includes the RRID, RDR name, PubMedID (PMID), DOI, article publication date, journal name, journal ID, ESSN/ISSN, article title, and snippet.

A working group from the Global Library of Underwater Biological Sounds effort collaborated with the World Register of Marine Species (WoRMS) to create a global inventory of species categorized by known underwater sonifery. The inventory is provided as an ecological trait on WoRMS (MarineSpecies.org), where it may be regularly updated. The purpose of this Borealis dataset is to store spreadsheet versions of any updates to the data on WoRMS. The methods used to create the dataset are described in a peer-reviewed article (Looby et al. 2023. Scientific Data. 10:892. https://doi.org/10.1038/s41597-023-02745-4). The original dataset is available in figshare (https://doi.org/10.6084/m9.figshare.c.6704481.v1) to serve as a permanent record of what was peer-reviewed and will therefore not be updated.

SPSS file represents responses from 516 participants gathered by a quantitative survey method. The questionnaire provided context for the study and explained why collecting this data was important. At the bottom of this section, everyone’s approval on participating in the survey and using their responses in survey results in a public setting was requested. As this survey was voluntary and anonymous, the participants had a choice not to participate in it by simply declining the request and withdrawing themselves from the survey without any consequences. After the participants gave their approvals, the following segment (Section II) was permitted to proceed. The next section asked students about their five self-related characteristics which were the independent categorical variables 1) demographic characteristics: gender, 2) socioeconomic characteristics (SES), 3) geographic characteristics: native place, 4) academic characteristics related to higher secondary academic performance (pre-college performance) 5) the psychological and behavioral characteristics. It further constituted four sub-questions related to students’ information about 5-1) the engineering major enrolled in 5-2) priority for type of curriculum delivery 5-3) the most valuable human influence, 5-4) the most effective information source. The next section encompassed Questions related to twelve ECs characteristics including proximity, location and locality, image and reputation, faculty profile, alumni profile, campus placements, quality education, infrastructure and facilities, safety and security, curriculum delivery, value for money and lastly sustainability under COVID-19 was examined. To answer these questions, students were asked to rate on a Likert scale (1 to 5), where 1 represented minimum value and 5 showed maximum value.

Data Supporting Thesis http://dx.doi.org/10.26021/9958

This dataset contains 189 survey responses from a respository/ data managers' survey where we explored the current status, needs and challenges of research data repositories.

This report reviews the options available for the development of a technical infrastructure to support research data management (RDM) at the University of Sheffield. RDM, its situation within academic research and recent drivers towards change are defined. The processes involved in RDM and the elements of the supporting technical infrastructure are examined. The local context, of RDM technical infrastructure at the University of Sheffield and collaborating institutions, is explored. The report briefly describes the eighty most commonly used components of RDM technical infrastructure at UK HEIs. The report describes evaluations, reviews and comparisons of these components, gives examples of established RDM services and highlights the recent projects at UK HEIs which were involved in developing these services. This report focuses on the outcomes of projects at UK HEIs funded by the JISC ‘Managing Research Data’ programmes 2009-11 and 2011-2013. Generally the infrastructure architectures examined have been developed in response to the functional requirements derived from researcher workflows. Finally, recommendations for suitable technical infrastructure components are proposed. This is an updated version (2.4) of the document at http://dx.doi.org/10.6084/m9.figshare.1092561

GazeBaseVR is a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a series of five different ET tasks: (1) a vergence task, (2) a horizontal smooth pursuit task, (3) a video-viewing task, (4) a self-paced reading task, and (5) a random oblique saccade task. Many of these participants have also been recorded for two previously published datasets with different ET devices, and 11 participants were recorded before and after COVID-19 infection and recovery. GazeBaseVR is suitable for a wide range of research on ET data in VR devices, especially eye movement biometrics due to its large population and longitudinal nature. In addition to ET data, additional participant details are provided to enable further research on topics such as fairness. For more details regarding the experimental methodology, please refer to the corresponding manuscript.

This is the data repository for an undergraduate thesis and a paper entitled A Corpus-Based Syntactic Analysis of That-Relative Clause in the Corpus of Contemporary American English (COCA). If you use the data and figures in this repository for your research, please cite this work as follows:Christina, Silvia; Rajeg, Gede Primahadi Wijaya; Netra, I Made (2021): Datasets and figures for A Corpus-Based Syntactic Analysis of That-Relative Clause in the Corpus of Contemporary American English (COCA). figshare. Dataset. https://doi.org/10.6084/m9.figshare.14701392First supervisor: Dr. I Made NetraSecond supervisor: Gede Primahadi Wijaya Rajeg, Ph.D. The analysed data in this repository were randomly taken from the Corpus of Contemporary American English (Davies, 2010) which can be accessed through https://www.english-corpora.org/coca/. Kindly cite COCA as follows if you use COCA for your research:Davies, M. (2010) ‘The Corpus of Contemporary American English as the first reliable monitor corpus of English’, Literary and Linguistic Computing, 25(4), pp. 447–464. doi: 10.1093/llc/fqq018.