BackgroundThere is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and ResultsWe reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. ConclusionA substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

This collection contains five sets of datasets: 1) Publication counts from two multidisciplinary humanities data journals: the Journal of Open Humanities Data and Research Data in the Humanities and Social Sciences (RDJ_JOHD_Publications.csv); 2) A large dataset about the performance of research articles in HSS exported from dimensions.ai (allhumss_dims_res_papers_PUB_ID.csv); 3) A large dataset about the performance of datasets in HSS harvested from the Zenodo REST API (Zenodo.zip); 4) Impact and usage metrics from the papers published in the two journals above (final_outputs.zip); 5) Data from Twitter analytics on tweets from the @up_johd account, with paper DOI and engagement rate (twitter-data.zip).

Please note that, as requested by the Dimensions team, for 2 and 4, we only included the Publication IDs from Dimensions rather than the full data. Interested parties only need the Dimensions publications IDs to retrieve the data; even if they have no Dimensions subscription, they can easily get a no-cost agreement with Dimensions, for research purposes, in order to retrieve the data.

This data paper template refers to the national standards Data Paper Publishing Metadata (GB/T 42813-2023) and Academic Paper Writing Rules (GB/T 7713.2-2022), and also investigates and to some extent refers to the paper templates of domestic and foreign journals that publish data papers.

In order to analyse specific features of data papers, we established a representative sample of data journals, based on lists from the European FOSTER Plus project , the German wiki forschungsdaten.org hosted by the University of Konstanz and two French research organizations.
The complete list consists of 82 data journals, i.e. journals which publish data papers. They represent less than 0,5% of academic and scholarly journals. For each of these 82 data journals, we gathered information about the discipline, the global business model, the publisher, peer reviewing etc. The analysis is partly based on data from ProQuest’s Ulrichsweb database, enriched and completed by information available on the journals’ home pages.
One part of the data journals are presented as “pure” data journals stricto sensu , i.e. journals which publish exclusively or mainly data papers. We identified 28 journals of this category (34%). For each journal, we assessed through direct search on the journals’ homepages (information about the journal, author’s guidelines etc.) the use of identifiers and metadata, the mode of selection and the business model, and we assessed different parameters of the data papers themselves, such as length, structure, linking etc.
The results of this analysis are compared with other research journals (“mixed” data journals) which publish data papers along with regular research articles, in order to identify possible differences between both journal categories, on the level of data papers as well as on the level of the regular research papers. Moreover, the results are discussed against concepts of knowledge organization.

Arabian journal for science and engineering Acceptance Rate - ResearchHelpDesk - The Arabian Journal for Science and Engineering (AJSE) is a peer-reviewed journal owned by King Fahd University of Petroleum and Minerals and published by Springer. AJSE publishes twelve issues of rigorous and original contributions in the Science disciplines of Biological Sciences, Chemistry, Earth Sciences, and Physics, and in the Engineering disciplines of Chemical, Civil, Computer Science and Engineering, Electrical, Mechanical, Petroleum, and Systems Engineering. Manuscripts must be submitted in the English language and authors must ensure that the article has not been published or submitted for publication elsewhere in any format and that there are no ethical concerns with the contents or data collection. The authors warrant that the information submitted is not redundant and respects general guidelines of ethics in publishing. All papers are evaluated by at least two international referees, who are known scholars in their fields. About KFUPM King Fahd University of Petroleum & Minerals King Fahd University of Petroleum & Minerals (KFUPM) in Saudi Arabia is a leading educational organization for science and technology. The vast petroleum and mineral resources of the Kingdom pose a complex and exciting challenge for scientific, technical, and management education. To meet this challenge, the University has adopted advanced training in the fields of science, engineering, and management as one of its goals in order to promote leadership and service in the Kingdom’s petroleum and mineral industries. The University also furthers knowledge through research in these fields. In addition, because it derives a distinctive character from its being a technological university in the land of Islam, the University is unreservedly committed to deepening and broadening the faith of its Muslim students and to instilling in them an appreciation of the major contributions of their people to the world of mathematics and science. All areas of KFUPM - facilities, faculty, students, and programs - are directed to the attainment of these goals. About AJSE Arabian Journal of Science and Engineering - Sections King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE). AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world. Arabian Journal of Science and Engineering AJSE publishes twelve issues in both the Engineering (AJSE-Engineering) and Science (AJSE-Science) disciplines. The publication of thematic/special issues on specific topics is also considered. AJSE-Engineering AJSE-Engineering is a section of the Arabian Journal for Science and Engineering (AJSE). It publishes original contributions and refereed research papers in the disciplines of Civil, Chemical, Electrical, Mechanical and Petroleum Engineering, Computer Science and Engineering, and Systems Engineering. AJSE-Engineering publishes full-length original articles, review articles on specialized topics, technical notes, and technical reports. AJSE-Science Chemistry, Earth Sciences, Physics and now also: Biological Sciences AJSE-Science is a section of the Arabian Journal for Science and Engineering (AJSE). AJSE-Science publishes original contributions and refereed research papers in the disciplines of Chemistry, Earth Sciences, Physics, and now also Biological Sciences. AJSE-Science publishes full-length original articles, review articles on specialized topics, technical notes, and technical reports. Abstracted/Indexed in: Academic Search, CSA/Proquest, Current Abstracts, Current Contents/Engineering, Computing and Technology, Current Index to Statistics, EBSCO, Google Scholar, INIS Atomindex, OCLC, Science Citation Index Expanded (SciSearch), SCOPUS, Summon by Serial Solutions, Zentralblatt Math RG Journal Impact: 0.93 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2020 Available summer 2021 2018 / 2019 0.93 2017 1.12 2016 0.99 2015 1.04 2014 1.17 2013 0.63 2012 0.55 2011 0.58 2010 0.36 2009 0.37 2008 0.15 2007 0.16 2006 0.12 2005 0.25 2004 0.12 2003 0.20 2002 0.10 2001 0.14 2000 0.06 Additional details Cited half-life 4.50 Immediacy index 0.09 Eigenfactor 0.00 Article influence 0.14 Website http://www.kfupm.edu.sa/publications/ajse/ Website description Arabian Journal for Science and Engineering website Other titles Arabian Journal for science and engineering (online), AJSE ISSN 1319-8025 OCLC 264802239 Material type Periodical, Internet resource Document type Internet Resource, Journal / Magazine / Newspaper

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the Aurora Universities Network. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.

17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs.

The survey contains the following questions (see inside dataset for exact wording):

• Are you familiar with this SDG?
  • Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.
• Suggest research papers that are relevant for this SDG (upload list)
  • This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)
• Select research papers that are relevant for this SDG (radio buttons: accept, reject)
  • A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.
• Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Suggest SDG related glossaries with relevant keywords (text fields: url)
  • Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.
• Suggest improvements for the current queries (text field: suggestions per target)
  • We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.

In the dataset root you'll find the following folders and files:

• /00-survey-input/
  • This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.
• /01-raw-data/
  • This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.
• /02-aggregated-data/
  • This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.
• /03-scripts/
  • This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.
• /04-processed-data/
  • This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:

• SDG-survey-questions.pdf
  • This file contains the survey questions

  </li>
  <li><strong>SDG-survey-questions.doc</strong>
  <ul>
    <li>This file contains the survey questions</li>
  </ul>
  </li>
  <li><strong>SDG-survey-respondents-per-sdg.csv</strong>
  <ul>
    <li>Basic information about the survey and responses</li>
  </ul>
  </li>
  <li><strong>SDG-survey-city-heatmap.csv</strong>
  <ul>
    <li>Origin of the respondents per SDG survey</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications.txt</strong>
  <ul>
    <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong>
  <ul>
    <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-accepted.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-rejected.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-keywords.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-keywords.csv</strong>
  <ul>
    <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-keywords.csv</strong>
  <ul>
    <li>List of keywords researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-glossaries.csv</strong>
  <ul>
    <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-journals.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-journals.csv</strong>
  <ul>
    <li>As "selected-journals", this is the list of journals
  

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

Introduction: During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. Objective: To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Methodology: Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. Results: We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. Conclusions: There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.

This dataset contains information from scientific publications written by authors who have published papers in the RecSys conference. It contains four files which have information extracted from scientific publications. The details of each file are explained below:i) all_authors.tsv: This file contains the details of authors who published research papers in the RecSys conference. The details include authors' identifier in various forms, such as number, orcid id, dblp url, dblp key and google scholar url, authors' first name, last name and their affiliation (where they work)ii) all_publications.tsv: This file contains the details of publications authored by the authors mentioned in the all_authors.tsv file (Please note the list of publications does not contain all the authored publications of the authors, refer to the publication for further details).The details include publications' identifier in different forms (such as number, dblp key, dblp url, dblp key, google scholar url), title, filtered title, published date, published conference and paper abstract.iii) selected_author_publications-information.tsv: This file consists of identifiers of authors and their publications. Here, we provide the information of selected authors and their publications used for our experiment.iv) selected_publication_citations-information.tsv: This file contains the information of the selected publications which consists of both citing and cited papers’ information used in our experiment. It consists of identifier of citing paper, identifier of cited paper, citation title, citation filtered title, the sentence before the citation is mentioned, citing sentence, the sentence after the citation is mentioned, citation position (section).Please note, it does not contain information of all the citations cited in the publications. For more detail, please refer to the paper.This dataset is for the use of research purposes only and if you use this dataset, please cite our paper "Capturing and exploiting citation knowledge for recommending recently published papers" due to be published in Web2Touch track 2020 (not yet published).

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

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Repository of rejections of papers - letters and comments - that were ultimately accepted by a journal to educate others. Your rejection letters and comments are welcome. Some journals have begun including reviewers' comments with accepted papers to make the views of experts available to the reader. However, often the paper has been submitted to several journals and rejected before it is finally accepted. The rejection letters and comments are equally useful in helping to judge what kind of papers might be acceptable to a journal, and what kind of comments lead to rejections. Rather than hiding these low points in the trajectory of a scientific paper, this forum offers a place to publish these letters and comments to educate others.

This dataset underpins research undertaken by the Data Publishing team at Springer Nature which analysed the impact of Data Availability Statements on Nature journal editors, and how researchers choose to share their data.Mandatory Data Availability Statements were introduced by Nature journals in 2016 which require researchers to state how their data can be accessed.The dataset comprises of a single Excel file, which include the journal title, unique ID for each published article, subject areas, and the estimated time required to include a Data Availability Statement as reported by the journals' editorial staff. The median time per journal is also calculated.The full text of the Data Availability statement is included, and the statements are coded according to the data sharing method described.This dataset supports a paper that has been peer reviewed and accepted for presentation at the International Digital Curation Conference 2018. The paper has been submitted to the International Journal of Digital Curation. At the time of dataset release the full paper is available as a preprint in BioRxiv.

The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, STRUCTURE. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000-fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested data...

Data for paper "Recommending Scientific Datasets Using Author Networks in Ensemble Methods" which is accepted by Data Science Journal. These data contains 1)MAKG (Microsoft Academic Knowledge Graph) co-author network (HDT/RDF format), 2)MAKG paper/dataset title collection (HDT/RDF format), 3) MAKG paper/dataset abstract collection (HDT/RDF format).

This is the dataset for the paper "Are data papers cited as research data? Preliminary analysis on interdisciplinary data paper citations" submitted to iConference 2025.

Journal of biological chemistry Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Biological Chemistry welcomes high-quality science that seeks to elucidate the molecular and cellular basis of biological processes. Papers published in JBC can therefore fall under the umbrellas of not only biological chemistry, chemical biology, or biochemistry, but also allied disciplines such as biophysics, systems biology, RNA biology, immunology, microbiology, neurobiology, epigenetics, computational biology, ’omics, and many more. The outcome of our focus on papers that contribute novel and important mechanistic insights, rather than on a particular topic area, is that JBC is truly a melting pot for scientists across disciplines. In addition, JBC welcomes papers that describe methods that will help scientists push their biochemical inquiries forward and resources that will be of use to the research community. Beyond the consideration of anyone particular article, our mission as a journal is to bring significant, enduring research to the scientific community. We believe it is our responsibility to safeguard the research we publish by providing high-quality review and maintaining strict standards on data presentation and deposition. It is our goal to help scientists disclose their findings in the most efficient and effective way possible by keeping review times short, providing editorial feedback on the manuscript text and promoting papers after publication. It is our aspiration to facilitate scientific discovery in new ways by exploring new technologies and forging new partnerships. The heart of this mission is the publication of original research in the form of Articles and Accelerated Communications, a subset of JBC’s articles that succinctly report particularly compelling advances across biological chemistry. Some JBC papers are also selected as Editors’ Picks, which represent top content in the journal and are highlighted with additional coverage. The journal publishes JBC Reviews to keep readers up to speed with the latest advances across diverse scientific topics; Thematic Series are collections of reviews that cover multiple aspects of a particular field. JBC also publishes Editorials and eLetters to facilitate communication within the biological chemistry community, Meeting Reports discussing findings presented at conferences, Virtual Issues to help readers find collections of papers on their favourite topics, and Classics and Reflections that honour the papers and people that have shaped scientific progress. Find more information and instructions about these content types here. Finally, JBC administers an award program established in honor of Herbert Tabor, JBC’s editor-in-chief from 1971-2012. The review process at JBC relies predominantly on editorial board members who have been vetted and appointed by the editor-in-chief and associate editors. Our editorial board members undergo a comprehensive training process to make our reviews as consistent, thoughtful and fair as possible for our authors. As of January 2021, JBC is a gold open access journal; the final versions of all articles are free to read without a subscription. The author versions of accepted research papers and JBC Reviews are also posted and freely available within 24 hours of acceptance as Papers in Press. JBC is owned and published by the American Society for Biochemistry and Molecular Biology, Inc. Abstract & Indexing Indexed in Medline, PubMed, PubMed Central, Index Medicus, The Science Citation Index, Current Contents - Life Sciences, SCOPUS, BIOSIS Previews, Clarivate Analytics Web of Science, and the Chemical Abstracts Service

The following articles dealing with GOP data have been written and published so far. Please note: These articles have been published with free access to public, while the full copyright policies of each journal apply.

Papers with limited access will be listed in the experiment 'gop_papers_lim_access'.

Abstract: Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.

PearRead is a dataset of scientific peer reviews. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.

International Journal of Scientific and Technology Research Publication fee - ResearchHelpDesk - IJSTR - International Journal of Scientific & Technology Research is an open access international journal from diverse fields in sciences, engineering, and technologies Open Access that emphasizes new research, development, and applications. Papers reporting original research or extended versions of already published conference/journal papers are all welcomed. Papers for publication are selected through peer review to ensure originality, relevance, and readability. IJSTR ensures a wide indexing policy to make published papers highly visible to the scientific community. IJSTR is part of the eco-friendly community and favors e-publication mode for being an online 'GREEN journal'. IJSTR is an international peer-reviewed, electronic, online journal published monthly. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching, and research in the fields of engineering, science, and technology. Original theoretical work and application-based studies, which contribute to a better understanding of engineering, science, and technological challenges, are encouraged. IJSTR Publication Charges IJSTR covers the costs partially through article processing fees. IJSTR expenses are split among peer review administration and management, production of articles in PDF format, editorial costs, electronic composition and production, journal information system, manuscript management system, electronic archiving, overhead expenses, and administrative costs. Moreover, we are providing research paper publishing in minimum available costing such as there are no charges for rejected articles, no submission charges, and no surcharges based on the figures or supplementary data. IJSTR Publication Indexing IJSTR ​​​​​submit all published papers to indexing partners. Indexing totally depends on the content, indexing partner guidelines, and their indexing procedures. This is the reason sometimes indexing happens immediately and sometimes it takes time. Publication with IJSTR does not guarantee that paper will surely be added indexing partner website. The whole process for including any article (s) in the Scopus database is done by the Scopus team only. Journal or Publication House doesn't have any involvement in the decision whether to accept or reject a paper for the Scopus database and cannot influence the processing time of paper. International Journal of Scientific & Technology Research RG Journal Impact: 0.31 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2018 / 2019 0.31 2017 0.34 2016 0.33 2015 0.36 2014 0.19 Is Ijstr Scopus indexed? Yes IJSTR - International Journal of Scientific & Technology Research Journal is Scopus indexed. please visit for more details - IJSTR Scoups