This dataset contains Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016, analyzed for type of statement.

Categories used to classify the data availability statements.

Figure 1 – Abundance Data Availability (2022). Note: 2020 also attached.

Raw data supporting the Springer Nature Data Availability Statement (DAS) analysis in the State of Open Data 2024. SOOD_2024_special_analysis_DAS_SN.xlsx contains the DAS, DOI, publication date, DAS categories and related country by Insitution of any author.SOOD 2024_DAS_analysis_sharing.xlsx contains the summary data by country and data sharing type.Utilizing the Dimensions database, we identified articles containing key DAS identifiers such as “Data Availability Statement” or “Availability of Data and Materials” within their full text. Digital Object Identifiers (DOIs) of these articles were collected and matched against Springer Nature’s XML database to extract the DAS for each article. The extracted DAS were categorized into specific sharing types using text and data matching terms. For statements indicating that data are publicly available in a repository, we matched against a predefined list of repository identifiers, names, and URLs. The DAS were classified into the following categories:1. Data are available from the author on request. 2. Data are included in the manuscript or its supplementary material. 3. Some or all of the data are publicly available, for example in a repository.4. Figure source data are included with the manuscript. 5. Data availability is not applicable.6. Data are declared as not available by the author.7. Data available online but not in a repository.These categories are non-exclusive: more than one can apply to any one article. Publications outside the 2019–2023 range and non-article publication types (e.g., book chapters) that were initially included in the Dimensions search results were excluded from the final dataset. Articles were included in the final analysis after applying the exclusion criteria. Upon processing, it was found that only 370 results were returned for Botswana across the five-year period; due to this low number, Botswana was not included in the DAS focused country-level analysis. This analysis does not assess the accuracy of the DAS in the context of each individual article. There was no manual verification of the categories applied; as a result, terms used out of context could have led to misclassification. Approximately 5% of articles remained unclassified following text and data matching due to these limitations.

Code and data availability for WIce-FOAM 1.0: a two-dimensional numerical model developed at the 5-kilometre scale using OpenFOAM-v2306, which couples the dynamics and thermodynamics of heterogeneous sea ice under wave forcing in the Antarctic marginal ice zone.

Here we present a summary of public data availability for agrobiodiversity-related indicators of regional agrifood systems. Data have been drawn from surveys, reports, and censuses in Bangladesh, India, Nepal, and Pakistan.

This PDF contains links to all the relevant data for the Evans et al. Sawyer Mill dam removal reservoir response manuscript. See the PDF for the cited figshare items and collections. Please reach out to Alexandra Evans (corresponding author of the manuscript) if you have any questions about the data or would like to collaborate on work that the data could be used for.These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector.

Read the full publication: https://doi.org/10.3390/data6110121

This record is for Approval for Access (AfA) product AfA445. The Water Resource Availability and Abstraction Reliability Cycle 2 dataset indicates whether, and for what percentage of time, additional water may be available for consumptive abstraction (subject to assessment of local risks) for each Water Framework Directive Cycle 2 water body. Each water body is colour coded as follows: • Green - Water available for licensing • Yellow - Restricted water available for licensing • Red - Water not available for licensing • Grey - Heavily Modified Waterbodies (and /or discharge rich water bodies) This data is not raw, factual or measured. It comprises of estimated or modelled results showing expected outcomes based on the data available to us. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

Responsible data sharing in clinical research can enhance the transparency and reproducibility of research evidence, thereby increasing the overall value of research. Since 2024, more than 5,000 journals have adhered to the International Committee of Medical Journal Editors (ICMJE) Data Sharing Statement (DSS) to promote data sharing. However, due to the significant effort required for data sharing and the scarcity of academic rewards, data availability in clinical research remains suboptimal. This study aims to explore the impact of biomedical journal policies and available supporting information on the implementation of data availability in clinical research publications This cross-sectional study will select 303 journals and their latest publications as samples from the biomedical journals listed in the Web of Science Journal Citation Reports based on stratified random sampling according to the 2023 Journal Impact Factor (JIF). Two researchers will independently extract journal data-sharing policies from the submission guidelines of eligible journals and data-sharing details from publications using a pre-designed form from Apr 2025 to Dec 2025. The data sharing levels of publications will be based on the openness of the data-sharing mechanism. Binomial logistic regression analyses will be used to identify potential journal factors that affect publication data-sharing levels. This protocol has been registered in Open Science Framework (OSF) Registries: https://doi.org/10.17605/OSF.IO/EX6DV.

The data include HBV-light parameter sets and (best) simulations at the outlet of the Upper Blue Nile basin using three rainfall products (ARC2, CHIRPS, and PERSIANN-CDR).

Data description

This data note describes the final citation network dataset analysed in the manuscript "What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives’"[1].

The data collection strategy used to construct the following dataset can be found in the associated manuscript [1]. These data were originally downloaded from the Web of Science (WoS) Core Collection via the library subscription of the University of Edinburgh via a systematic search methodology that sought to capture literature relevant to ‘knowledge co-production’. The dataset consists of 1,893 unique document reference strings (nodes) interlinked together by 9,759 citation links (edges). The network dataset describes a directed citation network composed of papers relevant to 'knowledge co-production', and is split into two files: (i) ‘KnowCo_node_attribute_list.csv’ contains attributes of the 1,893 documents (nodes); and (ii) ‘KnowCo_edge_list.csv’ records the citation links (edges) between pairs of documents.

1. ‘KnowCo_node_attribute_list.csv’ consists of attributes of the 1,893 nodes (documents) of the citation network. Due to the approach used to collect data, there are two types of node: (i) 525 nodes represent documents retrieved from WoS via the systematic search strategy, and these have full attribute data including their reference lists; and (ii) 1,368 documents that were cited >2 times by our 525 fully retrieved papers (see manuscript for full description [1]). The columns refer to:

Id, the unique identifier. Fully retrieved documents are identified via a unique identifier that begins with ‘f’ followed by an integer (e.g. f1, f2, etc.). Non-retrieved documents are identified via a unique identifier beginning with ‘n’ followed by an integer (e.g. n1, n2, etc.).

Label, contains the unique reference string of the document for which the attribute data in that row corresponds. Reference strings contain the last name of the first author, publication year, journal, volume, start page, and DOI (if available).

authors, all author names. These are in the order that these names appear in the authorship list of the corresponding document. These data are only available for fully retrieved documents.

title, document title. These data are only available for fully retrieved documents.

journal, journal of publication. These data are only available for fully retrieved documents. For those interested in journal data for the remaining papers, this can be extracted from the reference string in the ‘Label’ column.

year, year of publication. These data are available for all nodes.

type, document type (e.g. article, review). Available only for fully retrieved documents.

wos_total_citations, total citation count as recorded by Web of Science Core Collection as of May 2020. Available only for fully retrieved documents.

wos_id, Web of Science accession number. Available only for fully retrieved documents only, for non-retrieved documents ‘CitedReference’ fills the cell.

cluster, provides the cluster membership number as discussed within the manuscript, established via modularity maximisation via the Leiden algorithm (Res 0.8; Q=0.53|5 clusters). Available for all nodes.

indegree, total count of within network citations to a given document. Due to the composition of the network, this figure tells us the total number of citations from 525 fully retrieved documents to each of the 1,893 documents within the network. Available for all nodes.

outdegree, total count of within network references from a given document. Due to the composition of the network, only fully retrieved documents can have a value >0 because only these documents have their associated reference list data. Available for all nodes.

2. ‘KnowCo_edge _list.csv’ is an edge list containing 9,759 citation links between the 1,893 documents. The columns refer to:

Source, the citing document’s unique identifier.

Target, the cited document’s unique identifier.

Notes

[1] Bandola-Gill, J., Arthur, M., & Leng, R. I. (Under review). What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives. Evidence & Policy

Abstract

The dataset was derived by the Bioregional Assessment Programme from 'Streamflow unified NSW' dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Provides summary of the amount of \good\ quality stream flow data for selected gauging stations in the Richmond river basin.

Purpose

To highlight which gauging stations have long periods of record with good quality data.

Dataset History

This dataset is a summary of the unified dataset which has already been registered (see Lineage).

Dataset Citation

Bioregional Assessment Programme (2015) CLM - Richmond Streamflow data availability. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/8ebaa843-7a61-4813-be75-360759c79fef.

Dataset Ancestors

• Derived From CLM - NSW River Gauge pdf documents.

• Derived From CLM - Streamflow unified NSW

• Derived From CLM - NSW Office of Water Gauge Data for Tweed, Richmond & Clarence rivers. Extract 20140901

The table HI- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 767560 rows across 699 variables.

Mobility/Location data is gathered from location-aware mobile apps using an SDK-based implementation. All users explicitly consent to allow location data sharing using a clear opt-in process for our use cases and are given clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure only the highest quality of data is made available for analysis.

Record Count:90 Billion+ Capturing Frequency: Once per Event Delivering Frequency: Once per Day Updated: Daily

Mobility Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited interval (daily/weekly/monthly/quarterly).

Business Needs: Consumer Insight: Gain a comprehensive 360-degree perspective of the customer to spot behavioral changes, analyze trends and predict business outcomes. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape. Advertising: Create campaigns and customize your messaging depending on your target audience's online and offline activity. Retail Analytics Analyze footfall trends in various locations and gain understanding of customer personas.

Here's the data attributes: maid latitude longtitude horizontal_accuracy timestamp id_type ipv4 ipv6 user_agent country state_hasc city_hasc hex8 hex9 carrier

In this dataset, I exhibit the "Raw Data" and "Processed Data" for the toughness modification of high-performance PEI/PBT blends with PTFE.

Data availability for the paper titled "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles" by Peter A. Alpert et al. This repository contains all data tables and files necessary to reproduce plots. Also included are open source ".hdf5” files that contain all data for X-ray microscopy images and ".dat" files having the raw data for mie resonance scattering to derive size change and mass loss. Please see the "Readme.pdf" file for more information.

This document will provide you with some things to consider if you want or need to make your data available to others. This document is freely available under a Creative Commons license in PDF format.

These data quantify the results of a two year data archiving effort by a small group of researchers and students at the National Center for Ecological Analysis and Synthesis at UC Santa Barbara in collaboration with the Gulf Watch Alaska synthesis group and funded by the Exxon Valdez Oil Spill Trustee Council (EVOSTC). The EVOSTC was formed following the Exxon Valdez oil spill in Alaska in 1989. Since then, the EVOSTC has funded hundreds of projects and in 2012 we began a project to recover and archive the data collected during these EVOSTC funded projects. These data and analyses summarize the archiving project results and inform a manuscript (Funder imposed data publication requirements seldom inspire data sharing) in which we ask 5 main questions about the data collected from the Exxon Valdez Oil Spill Trustee Council funded projects: 1. Twenty-five years after the EVOS, for how many projects funded by EVOSTC can we collect data? 2. Are there certain research fields that are more likely to make data available than others? 3. Are there certain sectors that are more likely to make data available than others? 4. Is the availability of data correlated to how old the data are? 5. Why did people refuse to share their data?

The data here are a quantification of the responses to data outreach efforts, a data analysis script and results PDF as well as a figures script and output figures PDF.