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253 results for “data publishing”
Phylogenetic data for construction of bryophyte tree using published sanger data
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Conceptualizing relationships among hyporheic exchange, storage, and water age: data represented in published figures
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Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
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Data showcase papers published in the Mining Software Repositories (MSR) conference (v2.2)
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <ul> <li>citing_dp_dois_citations.txt: Strong and weak citations of (strong and weak) citation papers</li> <li>data_paper_clustering.csv: The clustering process of MSR data papers</li> <li>data_paper_clusters.csv: Clusters of MSR data papers</li> <li>data_papers.bib: Bibliographic details of MSR data papers, along with their assigned clusters (field 'cluster') and strong citations (field 'usedby')</li> <li>dp_dois_citations.txt: Strong and weak citations of MSR data papers</li> <li>msr-all: Bibliographic details of all MSR (data and non-data) papers</li> <li>ndp_dois_citations.txt: Strong and weak citations of MSR non-data papers</li> <li>ndp_rand_dois_citations.txt: Strong and weak citations of a randomly chosen MSR non-data paper weighted sample</li> <li>self-citations.txt: Strong citations of MSR data papers by their authors</li> <li>strong_citation_classification.csv: The classification process of strong citation papers according to the SWEBOK knowledge areas</li> <li>strong_citation_fields.csv: SWEBOK knowledge areas of strong citation papers</li> <li>strong_citations.bib: Bibliographic details of strong citation papers</li> <li>survey_questionnaire.pdf: The final survey questionnaire</li> <li>survey_responses.csv: Anonymized responses of the final survey questionnaire (Email addresses have been excluded for privacy reasons.)</li> <li>weak_citations_notes.bib: Weak citations of MSR data papers and the use they make</li> </ul>
New data on the publishing productivity of American sociologists
<p><br> <strong>OVERVIEW</strong><br> <br> This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).<br> <br> The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.<br> <br> <strong>USER NOTES</strong><br> <br> Please see our paper in <em>Scholarly Assessment Reports</em>, freely available at <a href="https://doi.org/10.29024/sar.36">https://doi.org/10.29024/sar.36</a> , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.<br> <br> The data file is a single Excel file with five worksheets: <em>Sampling</em>, <em>Articles</em>, <em>Books</em>, <em>Individuals</em>, and <em>Departments</em>. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.<br> <br> • The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.<br> <br> • <em>iType</em> is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). <em>nType</em> provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.<br> <br> • <em>Inst</em> is a four-digit institution code. The first digit corresponds to <em>nType</em>, and the last three digits allow for alphabetical sorting by institution name. <em>Indiv</em> is a one- or two-digit code that can be used to sort the individuals by name within each department. The <em>Inst</em>, <em>nType</em>, and <em>Indiv</em> codes are consistent across the five worksheets.<br> <br> • For binary variables such as <em>Full professor</em> and <em>Female</em>, 1 indicates <em>yes</em> (full professor or female) and 0 indicates <em>no</em> (associate professor or male).<br> <br> The five worksheets represent five distinct stages in the data compilation process. First, the <em>Sampling</em> worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the <em>Individual</em> column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the <em>Individual</em> column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).<br> <br> The <em>Articles</em> worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the <em>Journal</em> column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the <em>Journal</em> column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the <em>Journal</em> column but assigned a value of 1 in the <em>Excluded</em> column and a value of 0 in the <em>Article credit</em> and <em>HI article credit</em> columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The <em>N</em> and <em>i</em> columns represent, for each article, the number of authors (<em>N</em>) and the faculty member's place in the byline (<em>i</em>), as described in section A.8. The <em>CiteScore</em> and <em>Highest percentile</em> columns were used to identify high-impact journals, as indicated in the <em>HI journal</em> column. The <em>Article credit</em> and <em>HI article credit</em> columns are article counts, adjusted for co-authorship.<br> <br> The <em>Books</em> worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the <em>Book</em> column represents a book written by one of the 2,132 faculty. Each row without an entry in the <em>Book</em> column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the <em>Books</em> worksheet—<em>Book credit</em> and <em>HI book credit</em>—follow the same format as those in the <em>Articles</em> worksheet.<br> <br> The <em>Individuals</em> worksheet consolidates information from the <em>Articles</em> and <em>Books</em> worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—<em>Years since PhD</em>, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the <em>Individuals</em> worksheet.<br> <br> The <em>Departments</em> worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as <em>Female</em> and <em>Years since PhD</em> are presented as percentages or averages—<em>% Female</em> and <em>Avg years since PhD</em>, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.</p>
Newly discovered cichlid fish biodiversity threatened by hybridization with non-native species - Data supporting published version
<p><span><a name="_Hlk503794553"><span>Invasive freshwater fish systems are known to readily hybridize with indigenous congeneric species, driving loss of unique and irreplaceable genetic resources. Here we reveal that newly discovered (2013-2016) evolutionarily significant populations of Korogwe tilapia (<i>Oreochromis korogwe</i>) from southern Tanzania are threatened by hybridization with the larger invasive Nile tilapia (<i>Oreochromis niloticus</i>). We use a combination of morphology, microsatellite allele frequencies and whole genome sequences to show that <i>O. korogwe</i> from southern lakes (Nambawala, Rutamba and Mitupa) are distinct from geographically-disjunct populations in northern Tanzania (Zigi River and Mlingano Dam). We also provide genetic evidence of <i>O. korogwe</i> x <i>niloticus</i> hybrids in three southern lakes and </span></a><span>demonstrate heterogeneity in the extent of admixture across the genome. Finally, using the least admixed genomic regions we estimate that the northern and southern <i>O. korogwe</i> populations most plausibly diverged approximately 140,000 years ago, suggesting that the geographical separation of the northern and southern groups is not a result of a recent translocation, and instead these populations represent independent evolutionarily significant units. We conclude that these newly-discovered and phenotypically unique </span><span>cichlid populations are already threatened by hybridization with an invasive species, and propose that these irreplaceable genetic resources would benefit from conservation interventions.</span></span></p>
Data from: Reporting guidelines for community-based participatory research (CBPR) did not improve the reporting quality of published studies: a systematic review of studies on smoking cessation
<p>Although a guideline for reporting the results of community-based participatory research (CBPR) was published in 2010, the impact on the quality of reporting a CBPR on smoking cessation is unknown. Here we provide the raw data of a systematic review that assessed the impact of a 2010 community-based participatory research reporting guideline. on the quality of reporting a CBPR on smoking cessation. Specifically, we searched the MEDLINE, Embase, the Cochrane Central Register for Controlled Trials (CENTRAL), PsycINFO, and CINAHL databases and included articles published up to October 2018 (PROSPERO: CRD42019111668). We assessed reporting quality using a 13-item checklist.</p>
Ionosphere-thermosphere data published in 'The 2D evolution of thermospheric ∑O/N2 response to weak geomagnetic activity during solar-minimum observed by GOLD'
This dataset was used to generate plots for a paper in the Geophysical Research Letter, an AGU journal. It includes the percentage difference of column density ratio of O/N2 between quiet time (DOY 153 in 2019) and weak geomagnetic activity time (DOY 156 in 2019), together with the meridional wind, zonal wind, meridional component and the zonal component of E cross B drift at pressure level -1.375 (160-170 km).
Ionosphere-Thermosphere Data Published in "Challenges to Understanding the Earth's Ionosphere and Thermosphere"
<p>These data sets accompany an AGU Centennial publication "Challenges to Understanding the Earth's Ionosphere and Thermosphere" by R.A. Heelis and A. Maute. The data file tiegcm_totTgcm.s_181_12UT_GCpaper.nc is output from a thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation described in the publication (DOI:10.1007/s11214-017-0330-3). The simulation is for 2009 solar minimum conditions. The ionospheric current and neutral wind in file "heelis_empirical_f130_12UT_extracted.nc" is based on a stand-alone dynamo model (described in doi:10.1002/2017JA024841) driven by the Horizontal Wind Model and ionospheric conductivities based on empirical models of the ionosphere (IRI) and the neutral atmosphere (MSIS). The calculation is for solar medium condition (F10.7=130) and DOY 181 under geomagnetic quiescent times.</p>
Overview of Research Data Sharing Policies Across Various Scientific Publishers
<p><strong>Context </strong></p><p>With the aim of advancing trust in published research, (scientific) publishers are developing Research Data Sharing Policies for their portfolio. Some publishers have the same policy across all journals. For example for the publishers Frontiers and Springer-Nature, a Data Availability statement is mandatory for all journals in their portfolio. While other publishers have different policy levels depending on the journal (e.g. Elsevier, American Chemical Society). In this list, one will find a short overview of Research Data Sharing Policies across publishers (mostly relevant to the Faculty of Science at Utrecht University).</p><p><strong>Limitations</strong></p><p>The list of publishers does not encompass all scientific publishers. Some Research Data Sharing Policies might be subjected to change.</p>
Supplementary data for: "Global growth, green goals: Shaping sustainable futures in international business education and research" published at BAR - Brazilian Administration Review
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Data for "Interplay of magnetic order and ferroelasticity in the spin-orbit coupled antiferromagnet K2ReCl6" published in PRB 109, 094409 (2024)
<p>The manuscript of paper is also available on arXiv:2207.11101</p> <p>The manuscript of this <a href="https://doi.org/10.1103/PhysRevB.109.094409">Phys. Rev. B paper</a> is also available on arxiv <a href="https://arxiv.org/abs/2207.11101">2401.03064</a>. </p> <p> </p>
Data for 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics
<p>Data (CSV) and scripts (R) underlying the analysis presented in 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics; see the publication for details.</p>
seltmann/taxonomy-darwin-core: A GitHub Approach to Publishing Darwin Core Formatted Occurrence Data for Taxonomic Studies
<p><strong>A GitHub Approach to Publishing Darwin Core Formatted Occurrence Data for Taxonomic Studies</strong></p> <p><strong>Description</strong><br> This repository contains a Darwin Core Archive template and instructions for revisionary taxonomists to use to publish their data as a Darwin Core Archive or Darwin Core Compliant CSV file. This repository contains the Darwin Core Archive for "A taxonomic revision of <em>Gryonoides</em> Dodd, 1920 (Hymenoptera: Scelionidae), with a review of the hosts of Teleasinae." The archive was produced by the authors from data contained in mx (Yoder et al. 2006–present).</p> <p><strong>Summary</strong><br> Darwin core archives have emerged as the accepted data-sharing standard for occurrence data about organisms. These occurrences could be observations or specimens in natural history collections. The standard is applied by natural history collections worldwide to share their data between various repositories, including Global Biodiversity Information Faculty (GBIF) and Integrated Digitized Bio collections (iDigBio). This repository can be repurposed as an example template for publishing material examined as a Darwin Core Archive, accessible for data aggregators, journals, and conforming to community standards. This method can be used for any occurrence dataset, such as material examined, species monitoring observation records, ecological observations, or natural history collection data.</p> <p><strong>How to Create a Darwin Core Archive Using this Repository</strong><br> 1- Fork repository on GitHub or download its contents.</p> <p>2- Use the occurrences.csv file as a template. Delete the *Gryonoides* data and add your own. Do not change the column number or order. It is ok to leave in extra columns that you do not use. The only columns that need to be filled out are <em>occurrenceID</em> and <em>BasisOfRecord</em>. Specific definitions of the field names can be found in the Darwin Core Documentation under the <a href="https://dwc.tdwg.org/terms/#occurrence">Occurrence Core</a>.</p> <p>3- Do not touch the meta.xml. This file describes the columns in the occurrences.csv file.</p> <p>4- Edit the eml.xml files to include information about your institution and project. </p> <p>5- Zip the folder to create the archive. If you are using GitHub you can use the zip function for the repository using Code -> Download ZIP.</p> <p>6- Validate the archive using the <a href="https://www.gbif.org/tools/data-validator">GBIF Data Validator tool</a>.</p> <p><strong>Citations</strong></p> <p>Yoder, M.J., Dole, K., Seltmann, K., and Deans, A. 2006-Present. Mx, a collaborative web based content management for biological systematists. http://mx.phenomix.org/index.php/Main_Page<br> </p>
Data for the article on 'Nominal and pronominal negative concord, through the lens of Belizean and Jamaican Creole' by J. van der Auwera, published in Linguistics
<p>Data for the article on 'Nominal and pronominal negative concord, through the lens of Belizean and Jamaican Creole' by J. van der Auwera, published in Linguistics</p>
Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022
<p>Transformed raw data and R scripts for the paper Problem reports and team maturity in agile automotive software development published at CHASE2022</p> <p>15th International Conference on Cooperative and Human Aspects of Software Engineering, May 21–22, 2022, Pittsburgh, PA, USA}´</p>
IR data for the compounds published in "Dioxygen Activation by a Bioinspired Tungsten(IV) Complex"
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IR data of the compounds published in "Replacement of Molybdenum by Tungsten in a Biomimetic Complex Leads to an Increase in Oxygen Atom Transfer Catalytic Activity"
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IR data of the compounds published in "The Effect of Pyridine-2-thiolate Ligands on the Reactivity of Tungsten Complexes toward Oxidation and Acetylene Insertion"
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Supplementary material 3 from: Petersen M, Hoffmann J, Glöckler F (2019) Access to Geosciences – Ways and Means to share and publish collection data. Research Ideas and Outcomes 5: e32987. https://doi.org/10.3897/rio.5.e32987
Programm of the two-day workshop Access to Geosciences: sharing and publishing data related to paleontological, mineralogical, and petrological objects using a common data standard. Museum für Naturkunde, 29-30 May 2017
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.