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141 results for “data policies”
Survey data on climate policy in three countries (Peru, Ghana, Philippines) within the project "Sustainable Middle Classes in Middle Income Countries: Transforming Carbon Consumption Patterns (SMMICC)"
<p>The unprecedented growth of the new middle classes in middle income developing countries implies a strong growth in both consumption and carbon emissions. The research project Sustainable Middle Classes in Middle Income Countries (SMMICC) investigates the drivers of carbon consumption choices of the new middle classes and policy options to decrease their carbon footprints, including the implementation of carbon taxes</p> <p>The research of the authors generated quantitative data on the acceptability of carbon taxes in three countries (Peru, Ghana, Philippines).</p> <p> </p> <p><strong>The data is provided in the following formats:</strong></p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.csv</p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.dta</p> <p>Additionally, the codebooks on variables of questionnaire and political parties in each country are attached in a csv format.</p>
Privacy Policies Paragraph containing Personal Data
<p>The data consists in crawled privacy policies from European privacy policies. They were split into paragraphs and annotated as containing or not personal data.</p> <p>The question that was asked to annotators was "Does this paragraph contain the explicit mention of specific personal data (e.g. name, phone number, social security, …) being collected?".</p> <p>A full description of the dataset can be found in D3.4 of the SMOOTH project</p>
IPBES Data Management Tutorials - Session 2.1: Introduction to the data management policy
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session,<em> Introduction to the data management policy</em><em>, </em>defines what a data management policy is and why it is important.</p>
IPBES Data Management Tutorials - Session 2.4: Implementation of the data management policy
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session on the<em> Implementation of the data management policy </em>provides a brief overview of the contents of the following chapters and how it all works together to improve the transparency and credibility of IPBES. </p>
IPBES Data Management Tutorials - Session 2.2: Why a data management policy for IPBES and how it concerns experts
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management policy </em>chapter provides an introduction of the IPBES data and knowledge management policy. It discusses why IPBES has a data and knowledge management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session, <em>Why a data management policy for IPBES and how it concerns experts, </em>provides background on what IPBES wants to achieve with its data and knowledge management policy and how it impacts experts within IPBES. The session now has a supplement which covers the development of the policy from version 1.0 to 2.0. </p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in Asia
<p>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. <br><br>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.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at <a href="https://doi.org/10.5281/zenodo.12566244">https://doi.org/10.5281/zenodo.12566244</a></p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, 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. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
FAIRsFAIR Policy Characterisation Data for D3.1
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. Policies are a crucial component in the FAIR ecosystem. To this end, FAIRsFAIR Work Package 3 (WP3): FAIR Data Policy and Practice carried out an analysis of the current data policy landscape at various levels (national, funder, publisher, institutional) to provide a snapshot of the situation in 2019 and to identify policy elements that support or hinder FAIR data practice. To provide a comparative baseline for reviewing the data policies of various stakeholders, the priority and supporting actions presented in the <em>Turning FAIR into Reality </em>(TFiR) action plan were employed. To assess how well the policies of different stakeholders currently reflect TFiR’s action plan, we carried out desk research to characterise policies. </p> <p>Based on a review of existing policy characterisation efforts, FAIRsFAIR defined a total of 42 policy features to be reviewed during our landscape assessment. Fourteen of these features were intended to capture information about the policy itself (e.g., title, year of introduction, machine-readability) and 28 policy features were reviewed in relation to the content of the policy. </p> <p>For each of the three stakeholder groups assessed (funders, publishers and HEIs) we reviewed 10-15 data policies to determine how far they currently reflect the stakeholder-specific actions as recommended in TFiR. We limited the number to be reviewed to reflect the time and effort available for this task. To enable us to assess whether a specific policy element had been addressed within a given stakeholder’s policy, a set of related indicators were developed and mapped to the policy features and related TFiR policy actions. </p> <p>The policies selected for analysis as part of the landscape assessment come from stakeholders currently demonstrating a relatively high level of engagement with the FAIR principles. These have been identified through desk research and through the FAIRsFAIR consortium partners’ subject knowledge and related activities. Based on their particular areas of expertise, UGOE identified publishers to be included in the review while SPARC Europe identified funding bodies and EUA identified HEIs. The 42 policies reviewed for this task should not be considered an exhaustive inventory of stakeholders currently engaged with Open Science and the FAIR principles. </p>
FAIRsFAIR Policy and Practice Survey 2019 data for D3.1_D3.2_D6.1
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. Landscaping activities have been a core activity during the initial stages of the FAIRsFAIR project and there has been close cooperation with colleagues in Work Package 3 carrying out the FAIR data policy and practice analyses; Work Package 2 on assessing FAIR requirements for interoperability and persistence; Work Package 6 on providing an overview of research communities’ needs for competence centres; and Work Package 7 on mapping RDM policies and support as well as FAIR education offerings in European HEIs. In particular, efforts were made to avoid duplication of effort across the three open consultation and survey instruments developed to assess the current landscape and to define a consistent approach to presenting our findings.</p> <p>As part of the landscape analysis, FAIRsFAIR ran an open consultation on Policy and Practice which was made available via EU Survey from August 2 - September 27, 2019. The consultation was also made accessible from the FAIRsFAIR website. A total of 106 responses were submitted by research support staff from across Europe and beyond. The open consultation included both open-ended and closed questions which sought to identify the different levels of maturity with regards to FAIR practices among disciplines, the range of policies that influence the way that researchers work, and the sources of support currently available to researchers. In developing the questions, FAIRsFAIR worked collaboratively with several related initiatives including the EOSC 5B projects, the Group of European Data Experts in RDA (GEDE), and the EOSC FAIR Working Group and Landscape Working Group to avoid duplication of effort in our information collection. FAIRsFAIR will continue to cooperate with these and other initiatives over the life of the project. The open consultation targeted members of the research support community to gain insights on their views and experiences in relation to implementing the FAIR principles.</p> <p>The open consultation questions were grouped under five broad themes:</p> <p>1) Practice</p> <p>2) Policy</p> <p>3) Repositories</p> <p>4) Skills</p> <p>5) Competence centres</p> <p>The data resulting from the open consultation has been used to inform three key FAIRsFAIR deliverables:</p> <ul> <li>D3.1 FAIR Data Policy Landscape Analysis</li> <li>D3.2 FAIR Data Practice Analysis</li> <li>D6.1 Overview of Needs for Competence Centre</li> </ul> <p> </p> <p> </p>
ROARMAP Open Access Policy data - Country list
<p>This data is a sub set of a dump from ROARMAP [http://roarmap.eprints.org/] taken on 24th August 2014.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>Number of Open Access policies and a ranking is shown for each country.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
EXIT_Dataset_Survey on key policies and perceptions_2023_Survey data Serbia
<p>Survey data generated in WP2 of the EXIT project in the context of Serbia.The purpose of the survey was to collect data on the perceptions of policies used to address territorial inequalities in Serbia. </p>
Data for "Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 recovery packages"
<p>This dataset contains the underlying data for the following publication: Tanaka, K., C. Azar, O. Boucher, P. Ciais, Y. Gaucher, D. J. A. Johansson (2022) Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages. <em>Climatic Change</em> <strong>172, </strong>1 (2022). https://doi.org/10.1007/s10584-022-03355-6</p> <p>Earlier manuscripts were published as a preprint. https://arxiv.org/abs/2104.08342</p>
Data and code for: "Improving the relevance of paleontology to climate change policy"
<p>Data and code for the article: "Improving the relevance of paleontology to climate change policy". [Link here]</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
Trade policy announcements can increase price volatility in global food commodity markets (Replication Data)
<p>Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":</p> <ul> <li>Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)</li> <li>Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)</li> <li>Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report</li> </ul>
Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Bioenergy-induced land-use change emissions with sectorally fragmented policies</strong></p> <p>by <em>Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler</em></p> <p>published in <em>Nature Climate Change </em>(2023).</p> <p><em><strong>ModelRuns_remind </strong></em>(directory) contains all REMIND model runs of the scenarios underlying the paper.</p> <p><em><strong>ModelRuns_magpie </strong></em>(directory) contains all MAgPIE model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper.</p> <p><em><strong>ScenarioMapping.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).</p>
Result data related to "Bersalli et al. (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany"
<p>Result data related to "Bersalli et al (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany". The following files are included:</p> <ul> <li>"Energy policy 2020-21 Germany-Spain.xlsx": Policy measures supporting clean energy during the Covid-19 pandemic in Germany and Spain. Data from <a href="http://energypolicytracker.org/">EnergyPolicyTracker.org</a>, amended by the authors.</li> <li> "factors.csv": Time series of emissions, population, GDP, energy intensity, and carbon intensity. Data derived from BP and Eurostat.</li> <li>"multiplicative-contribution-factors.csv": Time series of relative growth in the factors.</li> <li>"relative-cumulative-contribution-factors.csv": Time series of cumulative relative growth in the factors.</li> <li>"periods.csv": Relative growth in the factors during the global financial and COVID19 crises and before (pre) and after (post) the global financial crisis.</li> </ul>
POLIcy design ANNotAtions (POLIANNA): Towards understanding policy design through text-as-data approaches
<p>The POLIANNA dataset is a collection of legislative texts from the European Union (EU) that have been annotated based on theoretical concepts of policy design. The dataset consists of 20,577 annotated spans in 412 articles, drawn from 18 EU climate change mitigation and renewable energy laws, and can be used to develop supervised machine learning approaches for scaling policy analysis. The dataset includes a novel coding scheme for annotating text spans, and you find a description of the annotated corpus, an analysis of inter-annotator agreement, and a discussion of potential applications in the paper accompanying this dataset. The objective of this dataset to build tools that assist with manual coding of policy texts by automatically identifying relevant paragraphs.</p> <p>Detailed instructions and further guidance about the dataset as well as all the code used for this project can be found in the accompanying <a href="https://doi.org/10.1038/s41597-023-02801-z">paper</a> and on the <a href="https://github.com/kueddelmaier/POLIANNA">GitHub project page</a>. The repository also contains useful code to calculate various inter-annotator agreement measures and can be used to process text annotations generated by <a href="https://inception-project.github.io/">INCEpTION</a>.</p> <p> </p> <p><strong>Dataset Description</strong></p> <p>We provide the dataset in 3 different formats:<br><br><em>JSON: </em>Each article corresponds to a folder, where the Tokens and Spans are stored in a separate JSON file. Each article-folder further contains the raw policy-text as in a text file and the metadata about the policy. This is the most human-readable format.</p> <p><em>JSONL: </em>Same folder structure as the JSON format, but the Spans and Tokens are stored in a <a href="https://jsonlines.org/">JSONL</a> file, where each line is a valid JSON document.</p> <p><em>Pickle: </em>We provide the dataset as a Python object. This is the recommended method when using our own Python framework that is provided on GitHub. For more information, check out the GitHub project page.</p> <p><br><strong>License</strong></p> <p>The POLIANNA dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. If you use the POLIANNA dataset in your research in any form, please cite the dataset.</p> <p> </p> <p><strong>Citation</strong></p> <p>Sewerin, S., Kaack, L.H., Küttel, J. <em>et al.</em> Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset. <em>Sci Data</em><strong>10</strong>, 896 (2023). https://doi.org/10.1038/s41597-023-02801-z</p>
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