The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

This FAIRsharing record describes: Data policy and author guidance for Discover Chemical Engineering by Springer Nature. Discover Chemical Engineering is part of the Discover journal series committed to providing a streamlined submission process, rapid review and publication, and a high level of author service at every stage. It is an open access, community-focused journal publishing research from across all fields relevant to chemical engineering. This journal recommends 1) the resources listed directly within this record, and 2) all resources listed in this record's parent policies (see the "extends" relationship).

Poster presented at the Research Data Alliance 5th Plenary Meeting, March 2015. To best encourage data publishing by scientific researchers, the burden of submission needs to be low. Data archiving at the time of and in conjunction with article publication can be an effective means, by catching authors when they’re motivated and tying data submission into an already-familiar publication process. Here we share Dryad’s experiences with integrating journals using various workflows.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Questionnaire data for DRYAD 2018 07 26Data from questionnaires sent to authors of papers in the research domain of Ecology. The column header are mostly self-explanatory. They are: ResponseID: a serial number linking rows corresponding to multiple submissions of the same manuscript (MS). Note that some MSs were submitted repeatedly to the same journal. Invited MS: Was the manuscript invited by the journal for publication? Round start: A serial number for the round of submission of each manuscript, beginning with 1, and incrementing upwards. Round end: A serial number for the outcome of each submission round, beginning with 2 and incrementing upwards. Note that the final round of submission is given identifier 99. Journal start: To which journal was the MS submitted in this round? Journal end: To which journal was the MS next submitted? Note that this may be the same as Journal Start, if the manuscript was not rejected. If it was rejected, then it will (typically) differ. JIF st...

Data set for Numerical Simulations of Laboratory-Scale, Hypervelocity-Impact Experiments for Asteroid-Deflection Code Validation Manuscript

Scientific Data Management System Market Outlook

As per our latest research, the global Scientific Data Management System (SDMS) market size has reached USD 4.2 billion in 2024, demonstrating robust growth with a Compound Annual Growth Rate (CAGR) of 11.8% anticipated throughout the forecast period. The market is projected to attain a value of USD 11.6 billion by 2033, driven by the increasing complexity of scientific research data and the growing demand for efficient data management solutions. This expansion is underpinned by the rapid digital transformation in the life sciences sector, the proliferation of data-intensive research, and the critical need for data integrity and compliance in regulated environments.

One of the primary growth factors for the Scientific Data Management System market is the exponential surge in data generation across scientific research domains such as genomics, proteomics, drug discovery, and clinical trials. Research organizations and pharmaceutical companies are generating petabytes of data daily, necessitating advanced data management platforms to ensure seamless data capture, storage, retrieval, and analysis. The integration of SDMS with laboratory information management systems (LIMS) and electronic lab notebooks (ELN) further enhances workflow efficiency and data traceability, bolstering adoption rates among both large enterprises and smaller research institutions. Moreover, the growing emphasis on data standardization and interoperability is compelling organizations to invest in robust SDMS platforms that can handle diverse data formats and facilitate collaborative research across geographies.

Another significant driver propelling the SDMS market is the stringent regulatory landscape governing scientific research, particularly in the pharmaceutical, biotechnology, and healthcare sectors. Regulatory bodies such as the FDA, EMA, and other international agencies mandate rigorous data documentation, audit trails, and data integrity protocols to ensure the reliability of research outcomes and patient safety. SDMS platforms are designed to address these compliance requirements, offering features such as automated audit trails, secure data access controls, and comprehensive reporting capabilities. The increasing prevalence of multi-site clinical trials and the globalization of research initiatives are further amplifying the need for centralized, standardized data management systems that can support regulatory submissions and streamline data governance.

The ongoing advancements in artificial intelligence, machine learning, and cloud computing are also playing a pivotal role in shaping the future of the Scientific Data Management System market. Modern SDMS solutions are leveraging AI-driven analytics to extract actionable insights from complex datasets, enabling researchers to accelerate hypothesis generation, identify novel biomarkers, and optimize experimental workflows. The adoption of cloud-based SDMS platforms is facilitating remote collaboration, real-time data sharing, and scalable storage solutions, making it easier for organizations to manage growing data volumes without significant infrastructure investments. These technological innovations are expected to drive further market expansion and foster the development of next-generation SDMS platforms tailored to the evolving needs of the scientific research community.

From a regional perspective, North America currently dominates the SDMS market, accounting for the largest share due to its well-established life sciences industry, significant R&D investments, and early adoption of advanced data management technologies. Europe follows closely, driven by robust government funding for scientific research and a strong presence of pharmaceutical and biotechnology companies. The Asia Pacific region is emerging as a high-growth market, fueled by increasing research activities, expanding healthcare infrastructure, and rising awareness about the benefits of SDMS solutions. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as organizations in these regions gradually embrace digital transformation and data-centric research practices.

International Journal of Scientific and Technology Research Abstract & Indexing - 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

From January 2014, Psychological Science introduced new submission guidelines that encouraged the use of effect sizes, estimation, and meta-analysis (the “new statistics”), required extra detail of methods, and offered badges for use of open science practices. We investigated the use of these practices in empirical articles published by Psychological Science and, for comparison, by the Journal of Experimental Psychology: General, during the period of January 2013 to December 2015. The use of null hypothesis significance testing (NHST) was extremely high at all times and in both journals. In Psychological Science, the use of confidence intervals increased markedly overall, from 28% of articles in 2013 to 70% in 2015, as did the availability of open data (3 to 39%) and open materials (7 to 31%). The other journal showed smaller or much smaller changes. Our findings suggest that journal-specific submission guidelines may encourage desirable changes in authors’ practices.

ABSTRACT This paper analyzes the indexing policies of Brazilian journals on Information Science. It considers the scarce approach to the subject in the context of scientific communication, as well as the pragmatic need to systematize the action of assigning keywords by the author of the publication. It aims to analyze the online guidelines for assignment of keywords to articles during the submission process. It is a descriptive research that follows a qualitative and quantitative methodology. It can be characterized as a documentary research as the data was collected from the publication policies and guidelines for authors that are made available by the journals. We also conducted a content analysis to systematize the collected data. The results reveal the existence of guidelines related to the number of terms, mostly connected to selection in indexing. This was not the case for the specifications of the depth of terms and the indexing language, despite the referral to the latter in a total of five journals that use a controlled language. We conclude that Brazilian journals of Information Science need to pay a greater attention to the implementation of indexing policies in order to provide a greater assertiveness to the authors, especially during the attribution of keywords.

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats.The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

A list of NIH-supported repositories that accept submissions of appropriate scientific research data from biomedical researchers. It includes resources that aggregate information about biomedical data and information sharing systems. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of contact for further information or inquiries can be found on the websites of the individual repositories.

This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories.Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a self-deposit with curation service. The deposit workflow is as follows:Create your repository account.If you are a first-time depositor, complete the U of G Research Data Repositories Dataset Deposit Intake Form.Activate your Data Repositories account by logging in with your U of G username and password.Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories.Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away.Prepare your dataset.Assemble your dataset following the Dataset Deposit Guidelines. Use the README file template to capture data documentation.Create a draft dataset record.Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide.Submit your draft dataset for review.Dataset review.Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Guidelines using a standard curation workflow.The curator will collaborate with you to enhance the dataset.Public release.Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.

The ESS-DIVE sample identifiers and metadata reporting format primarily follows the System for Earth Sample Registration (SESAR) Global Sample Number (IGSN) guide and template, with modifications to address Environmental Systems Science (ESS) sample needs and practicalities (IGSN-ESS). IGSNs are associated with standardized metadata to characterize a variety of different sample types (e.g. object type, material) and describe sample collection details (e.g. latitude, longitude, environmental context, date, collection method). Globally unique sample identifiers, particularly IGSNs, facilitate sample discovery, tracking, and reuse; they are especially useful when sample data is shared with collaborators, sent to different laboratories or user facilities for analyses, or distributed in different data files, datasets, and/or publications. To develop recommendations for multidisciplinary ecosystem and environmental sciences, we first conducted research on related sample standards and templates. We provide a comparison of existing sample reporting conventions, which includes mapping metadata elements across existing standards and Environment Ontology (ENVO) terms for sample object types and environmental materials. We worked with eight U.S. Department of Energy (DOE) funded projects, including those from Terrestrial Ecosystem Science and Subsurface Biogeochemical Research Scientific Focus Areas. Project scientists tested the process of registering samples for IGSNs and associated metadata in workflows for multidisciplinary ecosystem sciences.more » We provide modified IGSN metadata guidelines to account for needs of a variety of related biological and environmental samples. While generally following the IGSN core descriptive metadata schema, we provide recommendations for extending sample type terms, and connecting to related templates geared towards biodiversity (Darwin Core) and genomic (Minimum Information about any Sequence, MIxS) samples and specimens. ESS-DIVE recommends registering samples for IGSNs through SESAR, and we include instructions for registration using the IGSN-ESS guidelines. Our resulting sample reporting guidelines, template (IGSN-ESS), and identifier approach can be used by any researcher with sample data for ecosystem sciences.« less

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

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

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Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Researchers seeking to share their data with coordinating centers such as the National Database for Autism Research (NDAR), face numerous barriers to establishing new connections and maintaining existing ones. We sought to dramatically reduce the time and money required to establish and maintain the interoperability of data between research centers, by establishing a process where manual recoding of data is replaced by data sharing instructions in the form of extraction and transformation scripts. Over the course of seven typical (20-60 subjects, 400-1000 fields each) data submissions to NDAR, the need for duplication, retranscription, or restructuring of the source data was fully eliminated. Separating the extraction and transformation scripts from data files also eradicated the impact of additional data collection on the time required to repeat successful transmissions. Revision controlled management of these scripts also provided a new benefit: traceability of the transformation process itself. Now, point-in-time retrieval of extraction scripts and explanations for modifications to the data sharing interface are possible. This method has proven to be successful and efficient for interfacing research data with NDAR. It presents little-to-no impact to transmitting investigators’ data, ensures high data integrity, trivializes the complexities of repeatedly modifying a growing dataset over time, and introduces traceability to the collaborative process of integrating two collections of data with one another.

First workshop on Research data* management for linked open science - DaMaLOS * and other research objects

Summarizing past submissions to our sister journal, "Journal of Occupational Health."

A single entry point for users to link to and find information about data submission processes at NCBI.