This dataset contains background data for a small study about how the recommendations for how to increase the FAIRness of research data are being adopted in scientific/scholarly communities. To get a rough indication of how large the group of Early Adopters of the FAIR Data Principles might be in Norway, I compared the number of unique authors of datasets published in 2019 with the number of unique authors of publications of research results in anthology chapters, articles and monographs (books) in the same year. As a use case, I chose my own university, UiT The Arctic University of Norway (UiT).

The Journals dataset includes information about all historical Journal titles from Springer Nature, including ones that have been decommissioned.See also: https://scigraph.springernature.com/explorer/datasets/data_at_a_glance/A journal record usually includes information about its publisher, imprint, license model, chief editor, external identifiers, subjects and impact factor when available.Version info:* http://scigraph.downloads.uberresearch.com/archives/current/TIMESTAMP.txt* http://scigraph.downloads.uberresearch.com/archives/current/LICENSE.txt

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)

This is a copy of the deposit licence agreed by all users of BathSPAdata.

This component contains the raw data required to reproduce all the results and visualisations from the project. All datasets are licensed under a Creative Commons Attribution 4.0 International Public License (CC-BY 4.0). Please read the README and LICENSE files before use.

ScienceDB data policy is maintained by ScienceDB data curation team. The policy includes Data Type, Codes of Conduct for Depositors, Data Review Criteria, Data License Agreements, How to cite data published on ScienceDB, Data Retraction and Others. The first version of the policy is published on November 7,2020. And the last update is on March 1, 2022.

Activity proposed for the Hanna UCG Facility is that required to bring the facility into compliance with regulations of the Wyoming Department of Environmental Quality, Land Quality Division for a research and development in-situ testing license. Since additional experiments are, currently, not planned for the Hanna Facility, activity will focus on groundwater and surface restoration of old burn sites. An extensive program (including drilling and coring) is planned for the collection of additional baseline data to further access the geology and hydrology of the proposed license area. Existing burn sites will be thoroughly monitored using new and existing wells in conjunctions with the groundwater restoration activities.

United States - Governmental Taxes and License Fees for Scientific Research and Development Services, All Establishments, Employer Firms (DISCONTINUED) was 1131.00000 Mil. of $ in January of 2017, according to the United States Federal Reserve. Historically, United States - Governmental Taxes and License Fees for Scientific Research and Development Services, All Establishments, Employer Firms (DISCONTINUED) reached a record high of 1131.00000 in January of 2017 and a record low of 491.00000 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Governmental Taxes and License Fees for Scientific Research and Development Services, All Establishments, Employer Firms (DISCONTINUED) - last updated from the United States Federal Reserve on July of 2025.

Data Science

## Overview

Data Science is a dataset for object detection tasks - it contains Matricule annotations for 240 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

Observations from iNaturalist.org, an online social network of people sharing biodiversity information to help each other learn about nature.

Observations included in this archive met the following requirements:

* Published under one of the following licenses or waivers: 1) https://creativecommons.org/publicdomain/zero/1.0/, 2) https://creativecommons.org/licenses/by/4.0/, 3) https://creativecommons.org/licenses/by-nc/4.0/

* Achieved one of following iNaturalist quality grades: Research

* Created on or before 2025-07-29 15:00:18 -0700

You can view observations meeting these requirements at https://www.inaturalist.org/observations?created_d2=2025-07-29+15%3A00%3A18+-0700&d1=1600-01-01&license=CC0%2CCC-BY%2CCC-BY-NC&quality_grade=research

This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Layer was later renamed (in 2013) from "vbi1970_2001c5_Intersect_N83" to "VBI_LUC_1973_2001_NAD83" to avoid temporal confusion and remove ESRI auto-naming appendage. FGDC Metadata: Identification Information: Citation: Citation information: Originators: John H. Porter Title: Change data layer for the Virginia Coast Reserve, 1973-2001 - VCR05133 *File or table name: vbi1970_2001c5_Intersect_N83 Publication date: 12/22/2005 *Geospatial data presentation form: vector digital data *Online linkage: \MAP1\d\jhp7e\vbi1970_2001c5_Intersect_N83.shp Description: Abstract: This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Purpose: To detect changes on the coast of Virginia. *Language of dataset: en Time period of content: Time period information: Multiple dates/times: Single date/time: Calendar date: 08/12/1973 Single date/time: Calendar date: 08/27/2001 Currentness reference: ground condition Status: Progress: Complete Maintenance and update frequency: None planned Spatial domain: Bounding coordinates: *West bounding coordinate: -76.112114 *East bounding coordinate: -75.135130 *North bounding coordinate: 38.237583 *South bounding coordinate: 37.046598 Local bounding coordinates: *Left bounding coordinate: 402666.874551 *Right bounding coordinate: 487984.802095 *Top bounding coordinate: 4232184.738430 *Bottom bounding coordinate: 4100601.786647 Minimum altitude: -30 Maximum altitude: 30 Altitude units: m Keywords: Theme: Theme keywords: Change analysis Theme keyword thesaurus: None Place: Place keywords: Delmarva Peninsula Place keyword thesaurus: None Access constraints: VCR/LTER Data License required Use constraints: Bona fide scientific research. This is not a legal document Point of contact: Contact information: Contact person primary: Contact person: John Porter Contact organization: Virginia Coast Reserve Long-Term Ecological Research, University of Virginia Contact address: Address type: mailing and physical address Address: 291 McCormick Road Address: PO Box 400123 City: Charlottesville State or province: VA Postal code: 22904-4123 Country: USA Contact voice telephone: 434-924-8999 Contact facsimile telephone: 434-982-2137 Contact electronic mail address: jhp7e@virginia.edu Data set credit: John H. Porter, Virginia Coast Reserve Long-Term Ecological Research, University of Viriginia, Charlottesville, VA 22904 USA *Native dataset format: Shapefile *Native data set environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535 Cross reference: Citation information: Title: VCR05113 - Change analysis of the Virginia Coast 1973-2001 Back to Top -------------------------------------------------------------------------------- Data Quality Information: Positional accuracy: Horizontal positional accuracy: Horizontal positional accuracy report: 60-m pixels were used for the 1973 image. Quantitative horizontal positional accuracy assessment: Horizontal positional accuracy value: 60 Horizontal positional accuracy explanation: 60-m pixels were used for the 1973 image. Lineage: Process step: Process description: Dataset copied. Back to Top -------------------------------------------------------------------------------- Spatial Data Organization Information: *Direct spatial reference method: Vector Point and vector object information: SDTS terms description: *Name: vbi1970_2001c5_Intersect_N83 *SDTS point and vector object type: G-polygon *P

Graph and download economic data for Governmental Taxes and License Fees for Other Professional, Scientific, and Technical Services, All Establishments, Employer Firms (DISCONTINUED) (EXPGTLEF5419ALLEST) from 2012 to 2017 about licenses, science, fees, employer firms, professional, establishments, tax, expenditures, government, services, and USA.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebookv2.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydatav2.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydatav2.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL. Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level. EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues. EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services. For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster. Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research. License: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.

A count of copyright licences applied to 149 Australian University Journals published free on the internet in 2015, indicates that 19 Journals use a Creative Commons licence that meets the requirements of open access defined by the Budapest Open Access Initiative. This dataset was collected as part of the project, Injecting Creative Commons Licencing into University Policy and Publishing.

Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.

Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under https://quest-open-earthsciences.charite.de/.

The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2022. These datasets consist of (i) "journal_articles.csv" and (ii) "non_journal_articles_outputs.csv", the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).

Data for the dashboard was obtained from the FU university bibliography (https://frub-berlin.primo.exlibrisgroup.com/), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was enriched with manually researched information. Data enrichment was different for journal articles and for non-journal-article publications. For journal articles, copyright/open licence information was added and open access category information was checked and added or corrected. For non-journal-article outputs, missing PIDs were added and open access category information was checked and added or corrected.

The "data_dictionary_earth_sciences.csv" table documents all variables of each data file containing here.

Both for the dashboard, and in our following publications, we categorized "bronze" OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (https://quest-dashboard.charite.de/#tabStart) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under 10.5281/zenodo.10998219 [1]

[1] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. https://doi.org/10.5281/zenodo.10998219

EY Open Science Data Challenge 2024

## Overview

EY Open Science Data Challenge 2024 is a dataset for object detection tasks - it contains Buildings IQDU annotations for 248 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions:

Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services?

Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information.

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu