Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,929
datasets available to search
ShareScore release 0.7.1
Dataset results
10,929 results for “Communities”
Image-derived indicators of phytoplankton community responses to Pseudo-nitzschia blooms
<p>Data associated with the manuscript "Image-derived indicators of phytoplankton community responses to <em>Pseudo-nitzschia</em> blooms" submitted to the journal <em>Harmful Algae</em>. There is an additional R script that calculates an interaction metric as described in the paper. </p>
Topologies collected from 3 Community Networks
<p>This data-set contains graph topologies of several networks that were analysed in two scientific works and used in several more. </p> <p>The data in the 'topologies' folder contains two sets of data: The '2014' folder contains about 5000 snapshots of three community networks, namely Freifunk Wien, Freifunk Graz and ninux Rome. <br> This data-set was collected between 2014 and 2015 and is at the base of the work "A week in the life of three large Wireless Community Networks" (link to the paper below), it describes three large-scale wireless mesh networks running in three cities. <br> The data-set is fully described in the paper, here I report the information needed to use it.<br> - For FFWien and ninux, each snapshot is taken once every 5 minutes, for Graz, one every 10.<br> - one snapshot corresponds to the real state of the network in a specific moment, correlating the database of active nodes with the topology exported by the routing protocol. Some elaboration has been made to merge into one logical nodes some nodes that were running multiple instances of the routing protocol in the same physical location (see the paper for details)<br> - the format is the well known graphml XML format, you can open the files with networkx, gephi and many more tools<br> - the link weight represents the ETX metric (high = bad, see the paper)</p> <p>The network-evolution folder contains the network graphs collected for the two networks of Wien and Graz only, but in a different period of time, and with a much larger time-span between the snapshots. This data-set was used for the paper "On the Technical and Social Structure of Community Networks", and again, represents the physical structure of the network, annotated with link quality from the routing protocol. Format is graphml, metric is ETX.</p> <p>Finally, the mailing_list folder contains the ninux-ml.xml that contains the interactions in the mailing list of the ninux network, as described in the same paper. </p> <p>The second part of the data-set was collected and elaborated during the netCommons (see http://netcommons.eu) research project, while the first was collected before, but contributed to the results of the project too.</p> <p>If you use the data, pleas cite the relevant papers below.</p> <p>If you need more information, feel free to contact me:</p> <p>Leonardo Maccari, Assistant Professor @DISI, University of Trento<br> Tel: +39 0461 285323, www.disi.unitn.it/~maccari, gpg ID: AABE2BD7<br> leonardo.maccari(at)unitn.it.</p> <p>Related Papers:</p> <p>"A week in the life of three large Wireless Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2014Week.pdf</p> <p>"On the Technical and Social Structure of Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2016Technical.pdf</p>
Mapping practices of online community management
<p>Results of an online survey conducted during the period 13-31st March 2018. Responses were collected through a Google form; instructions and context were made available on a <a href="http://www.cottica.net/2018/03/13/mapping-online-community-management-practices-can-i-have-a-little-help-with-my-thesis/">web page</a>, the link of which was disseminated through Facebook, Twitter and on the e-mint mailing list (dedicated to professional online community managers on Yahoo. </p> <p>Each row of the file represents one questionnaire; each column represents one question.</p> <ul> <li>The first 9 questions are all of the format "To manage your online community, which of these courses of actions do you take, and how often?". The answers were given on a Likert-4 scale.</li> <li>The 10th question was "Do you want to add any other activity that uses up significant chunks of your community management time?". The answers were given in free form text.</li> <li>The 11th question was "How old is the community you manage? If you manage more than one, refer to the oldest." The answers were given as multiple choice, with three possible choices.</li> <li>The 12th question was "How large is the community you manage? If you manage more than one, refer to the largest.". The answers were given as multiple choice, with five possible choices.</li> </ul> <p>This work is part of my PhD Thesis.</p>
Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2
<p>This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a></p> <p>The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data.</p> <p>v1.2 includes a more detailed description of how the dataset was synthesised.</p>
bin3C - simulated community and associated sequencing datasets
<p>We simulated a human gut microbiome comprising 63 genomes from the GTDB annotated with an isolation source of faeces. No two genomes are more than 96% similar in terms of ANI.</p> <p>A Generalized Pareto distribution was used to model an abundance profile, which was assigned in random order to the references. There is a 50:1 difference between the most and least abundant member.</p> <p>Illumina shotgun and Hi-C reads were simulated using MetaART and sim3C (https://github.com/cerebis/sim3C).</p> <p>A sweep was performed over depth of coverage, by serially subsampling initial high depth readsets. Shotgun depth was parameterised by the most abundant at 250x, while Hi-C was parameterised by the number of pairs (200 million pairs).</p> <p>Shotgun was subsampled once, at half depth (125x), while Hi-C was subsampled 4 times (12.5, 25, 50, 100, 200 million pairs).</p> <p>The random seed used throughout was 12345.</p> <p>These simulated readsets were then analyzed using bin3C to retrieve metagenome-assembled genomes (MAGs). The resulting genome bins were validated using CheckM to estimate completeness and contamination.</p> <p> </p> <p> </p> <p> </p>
Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition
<p>This repository contains code to accompany the manuscript titled</p> <p><strong>Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition</strong></p> <p>by <strong>Carly D. Kenkel and Line K. Bay</strong><br> </p> <p>In this study, we used a phylogenetically controlled design to investigate the role of vertical symbiont transmission, an evolutionary mechanism predicted to enhance cooperation and holobiont fitness of reef-building corals. Six species of coral, three vertical transmitters and their closest horizontally transmitting relatives, were fragmented and subjected to a two-week thermal stress experiment. Symbiont cell density, photosynthetic function and translocation of photosynthetically fixed carbon between symbionts and hosts were quantified to assess changes in physiological metrics of fitness and cooperation. Amplicon sequencing of the <em>Symbiodinium</em> ITS-2 locus was used to investigate differences in symbiont community composition among focal species. We did not observe universally higher levels of cooperation in vertically transmitting species. However, the reduction in cooperation at the onset of bleaching was marginally associated with symbiont community diversity. Analysis of ITS2 amplicon sequence data suggest that it may not be vertical transmission <em>per se</em> that influences host-symbiont cooperation, but genetic uniformity of the symbiont community.</p> <p>Repository contents:</p> <ul> <li> <p><strong>TraitDataAnalysis.R:</strong> Annotated R script for generating figures and re-creating statistical analyses</p> <ul> <li> <p><strong>RsquaredGLMM.R:</strong> Accessory R script for running RsquaredGLMM analyses, called by <strong>TraitDataAnalysis.R</strong></p> </li> <li> <p><strong>NSF_RunningPam.csv</strong>: Input file for statistical analysis. Contains photophysiological data. Column headers are as follows:</p> <ul> <li> <p>Tank: Number of experimental tank in which experimental coral fragment was held</p> </li> <li> <p>Treatment: short-hand notation for sample treatments (e.g. ctrl1-5 = control temperature, genotypes 1-5)</p> </li> <li> <p>Water: source sump for temperature controlled water jackets for each set of treatment tanks</p> </li> <li> <p>Position: numerical rack position of coral fragment within experimental treatment tank</p> </li> <li> <p>Species: Coral species (Amil=<em>A. millepora</em>, Maqe=<em>M. aequituberculata</em>, Gast=<em>G. astreata</em>, Gach=<em>G. acrhelia</em>, Plob=<em>P. lobata</em>, Gcol=<em>G. columna</em>)</p> </li> <li> <p>Genotype: source colony origin of individual coral fragments within species</p> </li> <li> <p>Temp: experimental temperature treatment (ctrl: 27°C ; heat: 31°C)</p> </li> <li> <p>Treat: whether experimental corals received C14-labeled bicarbonate (bicarb), artemia or were sampled separately for Gene Expression Analysis (not presented in this manuscript)</p> </li> <li> <p>EQY: Effective quantum yield of <em>Symbiodinium</em> photosystem II as measured using PAM fluorometry</p> </li> <li> <p>Date: Actual calendar date of measure</p> </li> <li> <p>Transmission: coral symbiont transmission mode</p> </li> <li> <p>Reef: reef site of original coral collection</p> </li> <li> <p>Date: experimental date of measure</p> </li> </ul> </li> <li> <p><strong>TraitData.csv:</strong> Input file for statistical analysis. Contains all physiological trait data.</p> <ul> <li> <p>Includes columns as described above for the Running_Pam file in addition to columns containing raw trait data as described in the manuscript.</p> </li> </ul> </li> <li> <p><strong>TraitData_DaysAsCols.csv:</strong> Reformatted input file with trait data split by sampling day across columns</p> </li> </ul> </li> <li> <p><strong>DADA2Analysis.R:</strong> Annotated R script for generating figures and running ITS2 amplicon analyses</p> <ul> <li> <p>GeoSymbio_ITS2_LocalDatabase_verForPhyloseq.fasta: FASTA file of the GeoSymbio ITS2 reference database <a href="https://sites.google.com/site/geosymbio/">https://sites.google.com/site/geosymbio/</a>, formatted for use with the R prograom Phyloseq</p> </li> <li> <p>SeqVars_6Feb.fasta: FASTA file of identified sequence variants resulting from DADA2 analysis</p> </li> <li> <p>OutputDADA_6Feb.csv: Counts of sequence variants by sample</p> </li> <li> <p>Raw FASTQ paired end read files can be downloaded from NCBI's SRA: PRJNA338365</p> </li> </ul> </li> </ul>
Forschungsdaten/Visualisierungen zu: "Microblogging in den Informationswissenschaften - Quantitative Untersuchungen exemplarischer Communities auf Twitter"
<p>Dieses Datenset enthält zusätzliche Forschungsdaten zur Bachelorarbeit <a href="https://opus4.kobv.de/opus4-fhpotsdam/frontdoor/index/index/docId/2340">"Microblogging in den Informationswissenschaften - Quantitative Untersuchungen exemplarischer Communities auf Twitter"</a>. Eine genaue Erläuterung der einzelnen Dateien findet in der Arbeit selbst statt. Die hier enthaltenen Personendaten wurden nicht anonymisiert, enthalten jedoch rein öffentlich zugängliche Informationen.</p> <p>Zusätzlich sind hier einzelne Visualisierungen aus der Arbeit im PDF-Format enthalten.</p>
EMA-amplicon-based taxonomic characterisation of the viable bacterial community present in untreated and SODIS treated roof-harvested rainwater
<p>Dataset for publication: EMA-amplicon-based taxonomic characterisation of the viable bacterial community present in untreated and SODIS treated roof-harvested rainwater, Strauss et al. (2018). DOI: 10.1039/c8ew00613j.</p>
Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden – a prospective population-based study.
<p>Sepsis epidemiology study 2011-2012 Sweden</p> <p>Ljungström, Lars; Andersson, Rune; Jacobsson, Gunnar</p> <p> </p> <p>Data collected during the prospective "Sepsis Skaraborg study" performed 2011-2012 in the western region of Sweden. Adult patients admitted to the emergency department for suspicion of a community-onset sepsis were evaluated. The study was approved by the Regional Ethical Review Board of Gothenburg (376-11). The file includes data for patient characteristics, vital signs, biomarker measurements, cases of bacteremia, and patient classifications using Sepsis-2 and Sepsis-3 criteria.</p>
Short-term trends in great ape density in a community-based conservation area in the eastern Democratic Republic of the Congo
<p>Provided are the following supporting data and R script for "Short-term trends in great ape density in a community-based conservation area in the eastern Democratic Republic of the Congo":</p> <ol> <li>A dataset with summarized transect-based ape sign data, <br>SupportingInformation_ApeSigns.xlsx, with coordinates made approximate.</li> <li>A randomized dataset, <br>RandomizedData_INLA.csv, derived from the original, used as INLA-modeling input. </li> <li>A reproducible R script as used to generate the INLA models: SSupporting_Information_exampleINLA_Rscript_new.doc</li> </ol>
SCEC Community Fault Model (CFM)
<h1>Introduction</h1> <p>The Statewide California Earthquake Center (SCEC) Community Fault Model (CFM) is an object-oriented, fully three-dimensional geometric representation of active faults in California and adjacent offshore basins. For each fault object, the CFM provides triangulated surface representations (t-surfs) in several resolutions, fault traces in several different file formats (shape files, GMT plain text, and GoogleEarth kml), and complete metadata including references used to constrain the surfaces. The CFM faults are defined based on available data including surface traces, seismicity, seismic reflection profiles, well data, geologic cross sections, and various other types of data and models. The CFM serves SCEC as a unified resource for physics-based fault systems modeling, strong ground-motion prediction, probabilistic seismic hazards assessment (e.g., the USGS National Seismic Hazard Model), and many other uses. Together with the Community Velocity Model (CVM-H 15.1.0), the CFM comprises SCEC's Unified Structural Representation of the Southern California crust and upper mantle (Shaw et al., 2015).</p> <h1>Current Model Version: CFM 7.0</h1> <p>The current version of the SCEC CFM is version 7.0 (CFM 7.0), which builds on the previous CFM releases and serves as the latest update to Plesch et al. (2007). CFM 7.0 is a significant update as this is the first CFM to cover the entire state of California, spanning the Pacific-North American plate boundary from northern Mexico to the southern Cascadia subduction zone. This latest version has no changes to the southern California portion of the model, but now includes 113 new fault representations in central and northern California in the preferred model. These new central and northern California fault representations will undergo a community evaluation in 2024-2025, therefore, the central and northern California faults should be considered preliminary representations.</p> <p>CFM 7.0 contains three fully-documented sub models: preferred, ruptures, and alternatives. In total, CFM 7.0 comprises the following components: </p> <ol> <li> <p><strong>CFM 7.0 Preferred</strong>: A set of 556 fault objects that constitute the preferred set of active faults. These faults have attained preferred status based on past community evaluations or are new representations.</p> </li> <li> <p><strong>CFM 7.0 Ruptures</strong>: A set of 13 fault objects assembled from the CFM 7.0 preferred model that ruptured during selected significant historic events. These are not earthquake source models, but are representations of the entire fault surfaces where a significant historic rupture occurred. This model is intended to indicate which CFM fault objects were involved with selected significant historic ruptures.</p> </li> <li> <p><strong>CFM 7.0 Alternatives</strong>: A set of 39 alternative representations where structural differences have been proposed that could potentially significantly impact fault mechanics and associated seismic hazards. These alternative representations were selected based on community rankings following a comprehensive evaluation of the CFM that took place in May of 2022.</p> </li> </ol> <p>Including all sub models, the CFM 7.0 incorporates 608 fully-documented fault objects. If you use the CFM, we would appreciate you citing both Plesch et al. (2007) and the DOI where the archive is stored.</p> <h1>Directory Structure and Contents of the CFM Archive</h1> <p>The CFM archive directory structure is as follows:</p> <p><strong>doc/</strong><br>Documentation and metadata, which include an MS Excel spreadsheet with detailed metadata about each fault surface. Metadata for the preferred, rupture, and alternative models are provided in separate but otherwise identically formatted sheets within the file. All faults contain references to the works that helped to define the 3D fault surface geometry. More information about the metadata columns is provided in doc/README.txt</p> <p><strong>obj/preferred/</strong><br><strong>obj/ruptures/</strong><br><strong>obj/alternatives/</strong><br>These directories contain the model components for the preferred, rupture, and alternative models, respectively. Each model contains an identical directory structure, which is described below using the preferred model as an example.</p> <p><strong>obj/preferred/native/</strong><br>The CFM preferred fault surfaces in gocad tsurf format using the native mesh. The native mesh uses a variable mesh resolution. Smaller triangles generally indicate where a fault is well-constrained by data. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/500m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~500m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/1000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~1000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/2000m/</strong><br>The CFM preferred fault surfaces with a semi-regularized mesh of ~2000m resolution in gocad tsurf format. All tsurf files are provided in UTM zone 11 using the NAD27 datum (EPSG:26711).</p> <p><strong>obj/preferred/traces/</strong><br>Fault traces and upper tip lines (for blind faults) of the CFM preferred faults. While the CFM is a 3D model, it is often useful to make map-based visualizations of the model. The traces and blind faults are provided in several different formats described below.</p> <p><strong>obj/preferred/traces/gmt/<br></strong>Fault traces and blind faults in Generic Mapping Tools multiple segment file ASCII format (i.e., plain text).<br> .lonLat - Longitude/Latitude coordinates (WGS84 datum)<br> .utm - UTM zone 11 NAD27 datum (EPSG:26711)</p> <p><strong>obj/preferred/traces/kml/</strong><br>Fault traces and blind faults in Google Earth .kml format (WGS84 datum). The kml files also contain selected metadata as attributes which can be imported into QGIS. When a fault trace is clicked on in the Google Earth interface, a mini-webpage with metadata information will pop up.</p> <p><strong>obj/preferred/traces/shp/</strong><br>Fault traces and blind faults in GIS shapefile format (longitude/latitude coordinates, WGS84 datum).</p> <h1>CFM Contributors</h1> <p>The current and past versions of the CFM would not be possible without contributions from numerous SCEC community members. We would like to thank the following CFM contributors:</p> <p>Christine Benson, <a href="https://central.scec.org/user/bbryant">William Bryant</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dolan">James Dolan</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, Russell Graymer, <a href="https://central.scec.org/user/jhubbard">Judith Hubbard</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, Sam Johnson, <a href="https://central.scec.org/user/ylevy">Yuval Levy</a>, <a href="https://central.scec.org/user/lgrant">Lisa Grant Ludwig</a>, <a href="https://central.scec.org/user/hauksson">Egill Hauksson</a>, <a href="https://central.scec.org/user/tjordan">Thomas Jordan</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, Keith Knudsen, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/lindvall">Scott Lindvall</a>, <a href="https://central.scec.org/user/harold">Harold Magistrale</a>, James Lienkaemper, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/niemi">Nathan Niemi</a>, Stu Nishenko, <a href="https://central.scec.org/user/oskin">Michael Oskin</a>, <a href="https://central.scec.org/user/perry">Sue Perry</a>, <a href="https://central.scec.org/user/planansky">George Planansky</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/rockwell">Thomas Rockwell</a>, David Schwartz, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/pshearer">Peter Shearer</a>, Bob Simpson, <a href="https://central.scec.org/user/sorlien">Christopher Sorlien</a>, M. Peter Süss, <a href="https://central.scec.org/user/suppe">John Suppe</a>, <a href="https://central.scec.org/user/treiman">Jerry Treiman</a>, Jeff Unruh, Janet Watt, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, Chris Wills, <a href="https://central.scec.org/user/yeats">Robert Yeats</a>, and every colleague that has participated in a CFM community evaluation. We could not make the CFM without this community effort.</p> <h1>CFM Evaluators</h1> <p>Before assembling CFM 6.0 and subsequently CFM 7.0, a team of SCEC colleagues participated in a rigorous evaluation of CFM 5.3 in April-May of 2022. This evaluation was open to the SCEC community and focused on 23 critical fault representations where different proposed interpretations have the potential to significantly affect seismic hazards. This evaluation resulted in 14 new fault representations in the CFM 6.0 preferred model. The lower ranked representations are now provided in the CFM alternatives. We would like to thank the following CFM evaluators for volunteering their time and expertise to this process:</p> <p><a href="https://central.scec.org/user/sakciz">Sinan Akçiz</a>, <a href="https://central.scec.org/user/scarena">Sara Carena</a>, <a href="https://central.scec.org/user/cooke">Michele Cooke</a>, <a href="https://central.scec.org/user/dawson">Tim Dawson</a>, <a href="https://central.scec.org/user/jessaroni">Jessica Don</a>, <a href="https://central.scec.org/user/ajelliott">Austin Elliot</a>, <a href="https://central.scec.org/user/frost">Erik Frost</a>, <a href="https://central.scec.org/user/fuis">Gary Fuis</a>, <a href="https://central.scec.org/user/aganas">Athanassios Ganas</a>, <a href="https://central.scec.org/user/gath">Eldon Gath</a>, <a href="https://central.scec.org/user/alexhatem">Alex Hatem</a>, <a href="https://central.scec.org/user/sjanecke">Susanne Janecke</a>, <a href="https://central.scec.org/user/marc">Marc Kamerling</a>, <a href="https://central.scec.org/user/christos">Christodoulos Kyriakopoulos</a>, <a href="https://central.scec.org/user/mrlegg">Mark Legg</a>, <a href="https://central.scec.org/user/kluttrell">Karen Luttrell</a>, <a href="https://central.scec.org/user/madden">Chris Madugo</a>, <a href="https://central.scec.org/user/marshallst">Scott Marshall</a>, <a href="https://central.scec.org/user/meigsa">Andrew Meigs</a>, <a href="https://central.scec.org/user/nicholson">Craig Nicholson</a>, <a href="https://central.scec.org/user/nonderdo">Nate Onderdonk</a>, <a href="https://central.scec.org/user/absrp">Alba Rodríguez Padilla</a>, <a href="https://central.scec.org/user/plesch">Andreas Plesch</a>, <a href="https://central.scec.org/user/scharer">Kate Scharer</a>, <a href="https://central.scec.org/user/jshaw">John Shaw</a>, <a href="https://central.scec.org/user/sorlien">Chris Sorlien</a>, <a href="https://central.scec.org/user/wolfe_franklin">Franklin Wolfe</a>, <a href="https://central.scec.org/user/yule">Doug Yule</a>, <a href="https://central.scec.org/user/jzachariasen">Judy Zachariasen</a>.</p> <p> </p>
OpenAIRE Graph: dataset for research community in Virtual Human Twins
<p>This dataset contains metadata records of publications, research data, software and projects relevant for the research community in Virtual Twins in health.<br>The dump contains the records available in the <a href="https://dth.openaire.eu/" target="_blank" rel="noopener">OpenAIRE Gateway on Digital Twins in Health</a> of the <a href="https://www.edith-csa.eu/" target="_blank" rel="noopener">EDITH CSA project </a>of the European Commission (grant agreement n. 101083771).</p> <p>Records are identified via full-text mining and inference techniques applied to the <a href="https://graph.openaire.eu/">OpenAIRE Graph</a>.<br>The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating thousands of scholarly data sources world-wide.</p> <p>The dump consists of a tar archive containing gzip files with one json per line.<br>Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.10519297">https://doi.org/10.5281/zenodo.10519297</a>.</p>
Spatial Analysis on Kemanggisan Community Health Center
<p>This data was collected based on the condition during COVID-19 pandemic and New Normal Era (between 2022-2023)</p>
SC Transparency and Reproducibilty Community Survey
<p>Results of a survey administered to the SC conference community in August 2020 to all those who had participated in SC17, SC18, or SC19 technical programs. The survey participants were self-selected among 9,949 unique individuals. 204 individuals responded to at least one question (outside the consent question). The survey was conducted under Indiana University protocol #2005780098, “Assessing Reproducibility Initiative of IEEE/ACM Supercomputing Conference,” as an online survey protecting the anonymity of respondents. </p>
Structure and composition and carbon Stocks of woody plant community in assisted and unassisted ecological succession in a Tamaulipan thornscrub, Mexico
<p>In November of 2017, the structure and composition of woody plant communities were investigated through a floristic composition and diversity evaluation on three areas: a control area, an assisted ecological succession area and an unassisted ecological succession area.</p>
Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy (dataset)
<p>Dataset inherent to the following article:</p> <p>Medici, M., Canavari, C., Castellini, A., 2021. <em>Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy</em>, Journal of Cleaner Production, 316, 128233, DOI: <a href="https://doi.org/10.1016/j.jclepro.2021.128233">10.1016/j.jclepro.2021.128233</a></p>
Diachronic Corpus of Mission Statements for NC and FL Community Colleges
<p>This is a diachronic corpus of mission statements, philosophy statements, and purpose statements for community colleges in North Carolina and Florida. Texts date from the mid-1960s to 2020. Texts are indexed to IPEDS unit id. Texts for some years are missing. "OTM" means other than mission (which is typically a statement of purpose but may include statement of goals). Data were retrieved from archived catalogs and archived websites (e.g., Wayback Machine). The highest level of heading was used. For example, if a college published a statement of mission and a statement of purpose, the statement with the most prominent (typically the first) heading was used. </p>
Data from: Flattening the curve: approaching complete sampling for diverse beetle communities
<p><strong>DATA FROM:</strong></p> <p>Burner, R., J. Åstrom, T. Birkemoe, A. Sverdrup-Thygeson. 2021. Flattening the curve: approaching complete sampling for diverse beetle communities. <em>Insect Conservation and Diversity</em> <a href="https://doi.org/10.1111/icad.12540">https://doi.org/10.1111/icad.12540</a> </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>This research was funded by the Norwegian Environment Directorate as part of an ‘Agreement on monitoring hollow oaks and insects in hollow oaks’. The Norwegian University of Life Sciences (NMBU) workshop designed and produced the cross-pane flight intercept traps. Thanks to Sindre Ligaard for identifying the beetle species, and to Lindsay Burner, Ruben Roos, and Ross Wetherbee for assistance in the field. High-performance computing resources were provided by Frederick H. Sheldon and Louisiana State University (LSU HPC).</p> <p><strong>INFORMATION</strong></p> <p>This dataset contains all data necessary to reproduce the analysis in the resulting manuscript. Briefly, 110 insect traps were set for 3 months in a single forest stand in Ås, Norway in 2020. This dataset includes trap locations, number of individuals of each species captured in each trap, trap type, and forest covariates collected around the traps.</p> <p>For more detailed information see manuscript and README file.</p> <p>From abstract of manuscript:</p> <ol> <li>Insects are a hyper diverse and ecologically important group. Their high diversity, however, presents challenges in sampling methodology, because rare species are unreliably detected with low sampling effort. However, the relationship between effort and species detections, critical for effective monitoring and evaluation of population trends, is too seldom quantified.</li> <li>We sampled forest beetles for three months in a 4-ha stand of mixed deciduous forest in southeastern Norway using 110 flight intercept (four types) and Malaise traps, the highest trap density (29 traps/ha) that we have seen reported. We examined species accumulation curves to quantify the benefits of each additional trap, compared capture rates among several trap designs and trap emptying frequencies, and tested for spatial autocorrelation.</li> <li>In total we captured 566 beetle taxa (19,854 individuals) from 52 families, yet our species accumulation curve was only beginning to flatten. Trap types differed considerably in their effectiveness. Nevertheless, twenty of our most effective window traps detected 75% of all taxa in our dataset. We found no evidence of spatial correlation within the scale of the study (100 m radius), nor did trap-level forest covariates (5 m radius) explain much variation.</li> <li>This implies that low to moderate sampling effort dramatically underestimates species richness, but that a limited number of effective traps can nonetheless achieve relatively thorough sampling for some applications. Immediate trap surroundings and spacing appeared unimportant. But, insect ecologists should take particular care in selecting trap types and be cautious comparing studies that employed different trap types.</li> </ol> <p> </p>
Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction
<p>This is the data set supporting the analyses performed in the article entitled "Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction", by Natasha de Manincor, Alessandro Fisogni, and Nicole E. Rafferty, published in Ecology Letters (2023, 26:323-334, <a href="https://doi.org/10.1111/ele.14158">https://doi.org/10.1111/ele.14158</a>).</p> <p>The experiment has been performed in the greenhouse facilities at the University of California, Riverside, in 2021.</p> <p>The two treatments analyzed are ambient vs warmed (+ 4 °C), the focal pollinator species is <em>Osmia lignaria</em>, and the three focal plant species are <em>Collinsia heterophylla</em>, <em>Nemophila menziesii</em>, and <em>Phacelia campanularia</em>.</p> <p>Data are tab separated .txt files.</p>
Dataset used for: Effectiveness of Acute Malnutrition Treatment at Health Center and Community Level with a Simplified, Combined Protocol in Mali: An Observational Cohort Study
<p>This dataset contains the variables used in the analysis of the body composition and outcomes of the Acute Malnutrition Treatment at Health Center and Community Level with a Simplified, Combined Protocol in Mali pilot study, from December 2018 to December 2021</p>
ScienceDex guides
Understand access before you commit
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.