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10,929 results for “Communities”
November 2023 OCFL Community Meeting
<p>This month's community meeting included:</p><ul><li>Discussion around the <a href="https://github.com/OCFL/Use-Cases/">OCFL use cases</a> up for review by the community.</li><li>Focus on the <a href="https://github.com/OCFL/Use-Cases/issues/33">Package per version storage use case</a> and <a href="https://github.com/NationalLibraryOfNorway/ocfl-package-per-version-workgroup-notes">the analysis</a> done at the National Library of Norway and the University of Basel.</li></ul><p>Full notes and the recording can be found at: https://github.com/OCFL/spec/wiki/2023.11.08-Community-Meeting. Next community meeting: Wednesday December 13th 6pm ET / 3pm PT | Thursday Dec 14th 11pm GMT / 10am AEST (<a href="https://dateful.com/convert/est-edt-eastern-time?t=6pm&d=2023-12-13&tz2=Australia-Sydney">Convert to your time zone</a>)</p>
Awareness campaign video for local communities: Understanding and Preventing Rabies in the Menabe Region, Madagascar
<p>Cette courte vidéo est un outil utilisé pour la sensibilisation contre la rage. </p><p>Elle a été produite dans le cadre d'une recherche opération dans le sud ouest de Madagascar dans la région Menabe.</p><p>Elle présente en langue Malagasy, sous-titrée en Français:</p><p>- Ce qu'est la rage: la rage est une maladie due par une morsure d'un animal enragé ou le léchage de la plaie d'une personne par un animal enragé. Le chien est responsable de la rage dans près de 9 cas sur 10.</p><p>- actions à entreprendre à si une personne a été mordue par un chien</p><p>- les gestes à effectuer si une personne a été mordue par un chien</p><p>- elle insiste sur le fait que chez l'homme, la rage est traitable à 100% avant l'apparition du premier symptôme.</p><p>La vidéo est aussi disponible sur Youtube : </p><p><i>https://www.youtube.com/watch?v=RBK3Uywxszo&ab_channel=DaoudaKassi%C3%A9</i></p>
Dataset: Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay
<p><i><strong>"Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el cálculo del Índice de Pobreza Energética Multidimensional (MEPI) para el caso de estudio. </p><ol><li>MEPI_CarmenSoler_Data_2018_CHILECON2023.xlsx</li></ol><p>En el archivo, podrán encontrar los extraídos de los resultados de la encuesta realizada en el 2018 por un equipo de investigadores paraguayos (En el artículo podrán encontrar más información). Además de los datos, podrán ver todos los pasos y cálculos llevados a cabo para obtener los resultados obtenidos. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)
<p>The dataset contains data from fyke nets deployed in the Minho Estuary (Portugal) from 2010 to 2019. The fyke nets were used for fish sampling and data collection. The sampling frequency varied but, on average, data was collected weekly using five different fyke nets. However, due to technical issues (e.g. lost or damaged fyke nets), the sampling pattern is not constant, with some fyke nets staying underwater for shorter or longer periods, and occasionally having fewer than five fyke nets per parentEventID. The dataset includes various terms such as parentEventID, eventID, eventDate, year, startDayOfYear, endDayOfYear, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, DEIMS.iD, habitat, basisOfRecord, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort, occurrenceStatus, occurrenceID, organismQuantity, organismQuantityType, degreeOfEstablishment, vernacularName, scientificName, acceptedNameUsageID, taxonRank, kingdom, phylum, order, family, genus, and scientificNameAuthorship.</p>
Stable isotope data of a community of mid‑trophic level epipelagic species in the North Atlantic
<p>A total of 495 individuals from a guild of 14 species (fishes, squids and crustaceans) were sampled along the west and south Iberia (Portuguese coast) aboard a research vessel during 2014, 2016 and 2017. Two seasonal oceanographic surveys were conducted each year to sample pelagic mid-trophic level species during spring and autumn. Specimens were captured with trawl hauls, and information on date, time, position of capture (latitude and longitude) and depth (m) were recorded. The body mass (g) and total length (in fishes; mm), mantle length (in squids; mm) or carapace width (in crustaceans; mm) were recorded for each individual. Stable isotopes of carbon (δ13C) and nitrogen (δ15N) were analysed in the muscle of each individual. Lipids were removed from samples by successive rinses in a 2:1 chloroform–methanol solution. More details of the methodology can be found in:</p> <p>Ceia FR, Cherel Y, Silva AV, Garrido S, Angélico MM, da Silva JM, Laranjeiro MI, Ramos JA (2023) Drivers of niche partitioning in a community of mid-trophic level epipelagic species in the North Atlantic. Hydrobiologia, 850: 1583–1599. (DOI:10.1007/s10750-023-05160-3).</p>
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Community Package Lasry Dataset
<p>The Community package (<a href="https://github.com/SoloveyMaria/community">https://github.com/SoloveyMaria/community</a>) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization. </p><p>The dataset associated with this research has undergone peer review and has been published in the journal Nature Cancer. The publication can be accessed via the following link: <a href="https://doi.org/10.1038/s43018-022-00480-0">https://doi.org/10.1038/s43018-022-00480-0</a>. For access to the raw data, please visit: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381</a>. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted. This processed data serves as the input for the Community tool.</p>
CamTrapAsia: a dataset of tropical forest vertebrate communities from 239 camera trapping studies
<p>Dataset containing data from 239 camera trap studies conducted in tropical Asia. A total of 278,260 independent records were compiled, from 371 distinct species, comprising 232 mammals, 132 birds, and 7 reptiles. The accumulated trapping effort was 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal and far-eastern India.</p>
Combined RSEI Water Scores and American Community Survey, 2011–2021
<p>This dataset was created for a master's thesis in geography at the University of Utah. County-level RSEI scores and county demographics were combined for a spatiotemporal regression analysis. Counties in the contiguous United States and years 2011–2021 are included.</p> <p><em>Risk-Screening Environmental Indicators</em>. Annual, county-level RSEI risk scores for surface water were obtained from the EPA RSEI model through the <a href="https://edap.epa.gov/public/extensions/EasyRSEI_AllYears/EasyRSEI_AllYears.html">EasyRSEI Dashboard</a>. RSEI scores are <a>defined as the relative potential for health risks, given the estimated exposure calculated by the EPA</a>. A binary risk indicator was created, signifying whether the county had any risk in a given year.</p> <p><em>American Community Survey 5-year estimates</em>. County-level population characteristics were collected as five-year estimates from the American Community Survey, with annual estimates for the survey end year applying to each year from 2011–2021. We used the R tidycensus package to collect variables and geometry from the US Census Bureau API. Variables included <a>total population (scaled by 10,000), percent racial composition (American Indian or Alaska Native, or AIAN; Asian; Black or African American; Native Hawaiian or Other Pacific Islander, or NHPI; Some Other Race; Two or More Races; and White), and percent ethnic composition (Hispanic or Latinx). </a>Each census-defined racial category was collected as non-Hispanic or Latinx, such that each group was mutually exclusive.</p>
Journal of Climate and Community Development (JOCCD)
<p>cover of JOCCD</p>
2024.04.10 OCFL Community Meeting
<p>This month's community meeting included:</p> <ul> <li>Review and discussion of the V2 prioritized Use Cases</li> <li>File Manipulation and Object Challenges</li> <li>OCFL Object Forking and Deduplication</li> <li>Package Per Version for Managing Small Files</li> </ul> <p>The full notes and the recording can be found at <a href="https://github.com/OCFL/spec/wiki/2024.04.10-Community-Meeting">https://github.com/OCFL/spec/wiki/2024.04.10-Community-Meeting</a>. The next community meeting is Wednesday, May 8th, 3pm GMT / 4pm BST / 11am EDT / 8am PDT / Thursday, May 9th, 1am AEST (<a href="https://dateful.com/convert/est-edt-eastern-time?t=11am&d=2024-05-08&tz2=Oxford-UK" rel="nofollow">Convert to your timezone)</a></p>
A Dataset from Collected Events through the INCENTIVE Community Insider
<p>This dataset contains the collected events related to Citizen Science and public engagement, harvested from trusted online sources worldwide and provided by the <a href="https://toolkit.incentive-project.eu/community-insider/news-events">Community Insider</a> of the INCENTIVE Digital Toolkit.</p>
Dataset for "Gender and Gender Research in a Research Community: CSTT as a Case Study"
<p>This is the dataset used for and generated during our research for the following publication:<br>Francis Borchardt, Hanna Tervanotko and Saana Svärd "Gender and Gender Research in a Research Community: CSTT as a Case Study." In: Changes in Sacred Texts and Traditions: Methodological Encounters and Debates, eds. Martti Nissinen and Jutta M. Jokiranta. Resources for Biblical Study 106. SBL Press; Atlanta, USA. Pp. 517-544. 2024.<br><br></p>
Savanna-like Mammalian Community in the Mid-Miocene of Northern China--Supplementary Data
<p><strong><span>Supplementary Data 1:</span></strong><span> Stable Carbon and Oxygen isotopes data of Junggar Basin and Tongxin region, China.</span></p> <p><strong><span>Supplementary Data 2:</span></strong><span> Teeth hypsodonty of herbivorous mammals from China and Europe, including Sandelzhausen, Steinheim, Sansan, Madrid Basin, Qaidam Basin, Tunggur region, Baode, Lantian, Junggar Basin and Tongxin region.</span></p> <p><strong><span>Supplementary Data 3: </span></strong><span>Body mass of herbivorous mammals from different locations in China. </span></p>
Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons
<p>We present here the raw data and scripts to reproduce the results presented in the preprint "Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons" available on BioRxiv. Before using the scripts and associated data, we recommend reading the "readme" word document also available, which details the information available in the different data sheets. </p> <p>Preprint abstract : </p> <p>Coastal lagoons are transitional ecosystems that host a unique diversity of species and support many ecosystem services. Owing to their position at the interface between land and sea, they are also subject to increasing human impacts, which alter their ecological functioning. Because coastal lagoons are naturally highly variable in their environmental conditions, disentangling the effects of anthropogenic disturbances like eutrophication from those of natural variability is a challenging, yet necessary issue to address. Here, we analyze a dataset composed of macrobenthic invertebrate abundances and environmental variables (hydro-morphology, water, sediment and macrophytes) gathered across 29 Mediterranean coastal lagoons located in France, to characterize the main drivers of community composition and structure. Using correlograms, linear models and variance partitioning, we found that lagoon hydro-morphology (connection to the sea and lagoon surface), which affects the level of environmental variability (salinity and temperature), as well as lagoon-scale benthic habitat diversity (using macrophyte morphotypes) seemed to regulate macrofauna distribution, while eutrophication and associated stressors like low dissolved oxygen, acted upon the existing communities, mainly by reducing species richness and diversity. Furthermore, M-AMBI, a multivariate index composed of species richness, Shannon diversity and AMBI (AZTI's Marine Biotic Index) and currently used to evaluate the ecological state of French coastal lagoons, was more sensitive to eutrophication (18%) than to natural variability (9%), with nonetheless 49% of its variability explained jointly by both. To improve the robustness of benthic indicators like M-AMBI and increase the effectiveness of lagoon benthic habitat management, we call for a revision of the ecological groups at the base of the AMBI index and of the current lagoon typology which could be inspired by the lagoon-sea connection levels used in this study. </p>
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
Data and script: Catchment scale deforestation increases the uniqueness of subtropical stream communities
<p>These are datasets on benthic diatom and insect communities sampled in 100 streams along a gradient of land use intensification, ranging from streams in pristine forests to agricultural catchments in southeast subtropical Brazil. Data also include information on instream and land-use variables.</p> <p>In addition to the datasets, we also provide the R codes used to investigate whether compositional uniqueness (LCBD) and species contribution to beta diversity (SCBD) of stream diatoms and insects can be predicted by instream and land-use characteristics and by species traits and taxonomic relatedness, respectively. </p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
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.