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28,427 results for “group”
Data from: Group size and dispersal ploys: An analysis of commuting behaviour of the pond bat (Myotis dasycneme)
<p>This study aimed to provide a description on how Pond bats (<em>Myotis dasycneme</em>) disperse, how to recognize a commuting route, and details about the effort needed to make a complete survey of one commuting route. The study area covered the provinces of Zuid-Holland, Overijssel, Friesland, Noord-Holland, and Utrecht. During 6 years of study between 2002 and 2009, researchers and bat volunteers studied pond bats along several waterways (all waterways wider than 10 m) between known roosts and their hunting areas. All the observations were made between April and September, starting 20 min before sunset. During the entire observation effort, the time (in hours and minutes) and direction of each bat was recorded. The time that each bat passed the observation location was later transformed to minutes after sunset. The number of animals on commuting route was related to the number of animals present in their respective roost.</p> <p> </p> <p>Data are organized in 3 files: <strong>commuting data 10 minutes.csv</strong>, <strong>commuting data.csv</strong> and <strong>observations waddinxveen.csv</strong>. The variables in these data files are explained here:</p> <p>Date: the observation date</p> <p>Location description: description of the location</p> <p>X Y: The coordinates of the location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Long Lat: The coordinates of the location in longitude and latitude.</p> <p>Distance over water: commuting distance over water. For each route, the distance (d) over water between roost and observation location was measured from a topographical map and expressed in kilometres.</p> <p>Moon cover: the amount of moon cover, expressed in percentages.</p> <p>Roost location: the assumed location of the roost of the bats passing on their commuting route</p> <p>Max N of bats in roost: the max number of bats observed emerging from a roost.</p> <p>Sum N of bats over 10-minute interval: the sum of all the observed bats passing in one direction within a 10-minute interval</p> <p>Time after sunset in 10 min: the begin time of each interval, measured in minutes after sunset</p> <p>Peak time after sunset: the time of the observed peak in numbers of bats, in minutes after sunset.</p> <p>Area: the municipality near the observation location.</p> <p>Total N of pond bats on route: the total number of pond bats observed on route, in the given observation time. Including foraging and returning bats.</p> <p>Total N of commuting pond bats: the total number of bats observed commuting (excluding all other behaviours).</p> <p>Time of first bat minutes after sunset: the time of the first bat, measured in minutes after sunset.</p> <p>Duration of commuting: the time in hours between the first and the last bat observed commuting.</p> <p>Observation time: the total duration (in minutes) of the observation period.</p> <p>Moon phases: a 1–3 scale, where c1 is the new moon, c2 is the first quarter, c3 half moon, c4 is the last quarter and c5 is the full moon.</p> <p>Cloud cover: estimation of the cover, using the following three categories: c1-0%–25% cover (clear night sky or some isolated clouds), c2-25%–75% cover (several scattered clouds but not covering more than 75% of the night sky), and c3- 75%–100% cover (scattered clouds covering more than 75% of the night sky to a completely overcast night sky</p> <p>Observation type: observation of either emerging bats from a roost (roost) or bats observed on commuting route (commuting).</p> <p> </p> <p>In addition, we also provide 2 pdf’s containing the observation protocols (in Dutch) for counting emerging bats (<strong>Handleiding tellen van een groep meervleermuizen.pdf</strong>) and bats along a commuting route (<strong>Handleiding vliegroute telling.pdf</strong>). The protocols are intended for professionals and citizen scientists.</p>
Increments for Group Contribution Models (SUB-2017)
<p>Increments values and correction factors values for Group Contribution Models for Molar Refractivity, Heat of formation and LogP (n-octanol/water partition coefficient).</p>
Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland
<p><strong>Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland</strong></p> <p>In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).</p> <p> The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.</p> <p> In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education.</p> <p>Finally, groups were same-sex moderated.</p> <p>Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised. </p> <p>FG_CHE_1 Female <br>Female Group, Switzerland</p> <p>FG_CHE_2 Male<br>Male Group, Switzerland </p> <p>FG_DEN_1 Female <br>Female Groups, Denmakr</p> <p>FG_DEN_2 Male<br>Male Group, Denmark </p> <p>FG_DEN_3 Male <br>Male Group, Denmark</p> <p>FG_DEN_4 Mixed <br>Mix Male and Female Group, Switzerland</p> <p>FG_ESP_1 Male<br>Male Group, Spain</p> <p>FG_ESP_2 Male<br>Male Group, Spain </p> <p>FG_ESP_3 Female <br>Female Group, Spain</p> <p>FG_ESP_4 Female<br>Female Group, Spain</p> <p>FG_GBR_1 Female <br>Female Group, UK</p> <p>FG_GBR_2 Male <br>Male Group, UK</p> <p>FG_GER_1 Female <br>Female Group, Germany</p> <p>FG_GER_2 Male<br>Male Group, Germany</p> <p>FG_HUN_1 Female <br>Female Group, Hungary</p> <p>FG_HUN_2 Female <br>Female Group, Hungary</p> <p>FG_HUN_3 Male<br>Male Group, Hungary </p> <p>FG_HUN_4 Male<br>Male Group, Hungary </p>
Metzger and Ozpolat 2024 - raw imaging files for 40S group
<p>Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper.</p> <p>https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024 </p> <p>Publication: https://www.sciencedirect.com/science/article/pii/S0012160624001246</p>
Metzger and Ozpolat 2024 - raw imaging files for 60S group
<p><span>Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper. </span></p> <p><span>https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024 </span></p>
Rich subgroups in groups of order up to 2000
<p>This file contains information about nontrivial rich subgroups in groups G of order up to 2000, i. e., subgroups H with the property that the restriction of each character of G to H has a trivial constituent. Rich subgroups are studied in the paper <a href="https://arxiv.org/abs/2407.08040" target="_blank" rel="noopener">On Frobenius graphs of diameter 3 for finite groups</a>. The DOI for the data file is <a href="../records/12731413" target="_blank" rel="noopener">10.5281/zenodo.12731413</a>. The contents of the file is an array of length 3.</p> <ul> <li>The first entry is this description, an array of strings.</li> <li>The second entry is an array that lists the isomorphism types of the groups G; each entry has the format `[[n,i],[m1,m2,...,mk]]` meaning that G can be obtained as i-th group of order n in the <a href="https://gap-packages.github.io/smallgrp/" target="_blank" rel="noopener">small groups library</a>, and the representatives of the k conjugacy classes of rich subgroups in G have orders |m1|, |m2|, ..., |mk|; mj is positive if the j-th class of rich subgroups consists of diameter three subgroups of G, and negative otherwise.</li> <li>The third entry is an array that lists the isomorphism types of those groups G that are minimal w.r.t. inclusion with the property that they contain a rich subgroup; each entry has the format `[[n1,i1],[n2,i2],...,[nk,ik]]` meaning that the i1-th group of order n1, the i2-th group of order n2, ..., the ik-th group of order nk are minimal and that they are isoclinic.</li> </ul>
CLDF dataset derived from Starostin's "Annotated Swadesh Wordlists for the Karen Group" from 2017
<p>Cite the source of the dataset as:</p> <blockquote> <p>Starostin, George S. (2017): Annotated Swadesh Wordlists for the Karen Group. Moscow: The Global Lexicostatistical Database.</p> </blockquote>
Accompnaying Dataset for: Chemical Heredity as Group Selection at the Molecular Level
<p>Accompnaying Dataset for: Chemical Heredity as Group Selection at the Molecular Level. File descriptions are provided in the Appendix of [Markovitch, Witkowski and Virgo; Chemical Heredity as Group Selection at the Molecular Level, arXiv (2018)] (https://arxiv.org/abs/1802.08024).</p>
Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)
<p>Dataset used to perform the following manuscripts: </p> <p>- Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. <em>JMIR mHealth and uHealth</em>, <em>4</em>(3), e112. http://doi.org/10.2196/mhealth.5716</p> <p>rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework. </p>
Group Contribution Models for Tetrahymena Pyriformis Toxicity
<p>A data set of several models for Tetrahymena Pyriformis Toxicity (THPT) based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>)</p> <p>The main goal of the created THPT models is to illustrate Ambit-GCM software usage.</p> <p>Typically biological activity is not well modeled by means of additive schemes. In current modeling setup we demonstrate combination of group contribution scheme with external descriptors.</p>
Group Contribution Models for Standard molar enthalpy of formation of gas phase at 298.15K
<p>A data set of several models for Heat of formation based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>).</p> <p>QSPR models were built with a small training data set mainly for demonstration of Ambit-GCM software usage.</p>
The Polycomb Group protein Rnf2/Ring1b is essential for zebrafish development and cardiogenesis
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP (paired-end) sequencing data:</p> <ul> <li>NDC-TLF-3dpf-repl*: 5 biological replicates of RNA-seq data from 3dpf wild-type (TL background) whole embryo lysates</li> <li>TLF-3dpf-replicate*: 3 biological replicates of RNA-seq data from 3dpf wild-type (TL background) whole embryo lysates</li> <li>WT[12]-3dpf-ChIP-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT[12]-3dpf-H3K27me3-spikein*: 2 biological replicates of H3K27me3 ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT[12]-3dpf-Rnf2-spikein*: 2 biological replicates of Rnf2 ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> <li>WT2-3dpf-input-spikein*: input ChIP-seq data (with Drosophila H2Ay spike in) from 3dpf wild-type (TU/TF mixed background) whole embryo lysates</li> </ul>
Group size effects and critical mass in public goods games
<pre>This dataset accompanies the paper "Group size effects and critical mass in public goods games", https://doi.org/10.1038/s41598-019-41988-3 It records participant decisions in a set of binary one-shot Public Goods Games with curvilinear payoff function (see details in the paper). The explanation of the data fields is present also in the metadata of the file: # Cooperation: 0=defection, 1=cooperation, 99=the participant did not make the decision # Group: interacting group size (N) # Treatment: Critical Mass Nc # dropout: 1=the participant did not make the decision, 0=the participant made the decision # incompleted: 1=participant did not make all the decisions of the experiment; 0=participant did made all the decisions of the experiment # female: 1=female, 0=male </pre> <p> </p>
Datasets for the paper "Group formation on a small-world: experiment and modelling"
<p>Datasets required for reproducing all the results presented in the article "Group formation on a small-world: experiment and modelling" to be published in the Journal of the Royal Society Interface.</p> <p>Take a look into the included README for a description about the data format.</p>
Child mortality dataset (from the UN Inter-agency Group for Child Mortality Estimation database). June 2019
<p>This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (<a href="https://childmortality.org/data">https://childmortality.org/data</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong>Reference area: </strong>name of the country</p> <p><strong>Indicator:</strong> child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)</p> <p><strong>Sex: </strong>sex of the child (male, female and total)</p> <p><strong>Series name:</strong> name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]</p> <p><strong>Series year: </strong>year of survey/census/VR series</p> <p><strong>Observation value: </strong>value of indicator from survey/census/VR</p> <p><strong>Observation status:</strong> indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]</p> <p><strong>Series Category:</strong> category of survey/census/VR, and can be:</p> <ul> <li>DHS [Demographic and Health Survey]</li> <li>MIS [Malaria Indicator Survey]</li> <li>AIS [AIDS Indicator Survey]</li> <li>Interim DHS</li> <li>Special DHS</li> <li>NDHS [National DHS]</li> <li>WFS [World Fertility Survey]</li> <li>MICS [Multiple Indicator Cluster Survey]</li> <li>NMICS [National MICS]</li> <li>RHS [Reproductive Health Survey]</li> <li>PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]</li> <li>LSMS [Living Standard Measurement Survey]</li> <li>Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]</li> <li>Census</li> <li>VR [Vital Registration]</li> <li>SVR [Sample Vital Registration]</li> <li>Others [e.g. Life Tables]</li> </ul> <p><strong>Series type: </strong>the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)</p> <p><strong>Standard error: </strong>sampling standard error of the observation value</p> <p><strong>Series method: </strong>data collection method, and can be:</p> <ul> <li>Survey/census with Full Birth Histories</li> <li>Survey/census with Summary Birth Histories</li> <li>Survey/census with Household death</li> <li>Vital Registration</li> <li>Other</li> </ul> <p><strong>Lower and upper bound:</strong> the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).</p> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)</em></p>
LFM User Groups
<p>This is a subset of the LFM-1b LastFM dataset (http://www.cp.jku.at/datasets/LFM-1b/), which consists of the listening events (user_events.txt) for three user groups: 1,000 low-mainstream users (low_main_users.txt), 1,000 medium-mainstream users (medium_main_users.txt) and 1,000 high-mainstream users (high_main_users.txt). The mainstreaminess definition used here is the "M_global_R_APC" one from this paper: https://arxiv.org/ftp/arxiv/papers/1912/1912.06933.pdf</p> <p>The format of the three user files is "user , mainstreaminess_value"</p> <p>The format of the user-events file is "user \t artist \t album \t track \t timestamp"</p> <p>Example Python-code for analyzing this dataset as well as more information on the user groups based on mainstreaminess can be found here: https://github.com/domkowald/LFM_processing</p>
Data from: Paule et al. (2020) Phylogenomic insights into the Fascicularia-Ochagavia group (Bromelioideae, Bromeliaceae). Botanical Journal of the Linnean Society (doi: 10.1093/botlinnean/boz085)
<p>DNA sequence alignments as well as the input and output files which specify the different data partitioning schemes used for phylogenetic analyses in Paule et al. (2020) Phylogenomic insights into the Fascicularia-Ochagavia group (Bromelioideae, Bromeliaceae). Botanical Journal of the Linnean Society (doi: 10.1093/botlinnean/boz085).</p>
CLDF Dataset derived from Zhivlov's "Annotated Swadesh wordlists for the Ob-Ugrian group" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhivlov, M. (2011): Annotated Swadesh wordlists for the Ob-Ugrian group (Uralic family). The Global Lexicostatistical Database. Moscow: RGGU.</p> </blockquote>
Unistudium 2022: students groups logs on moodle
<p>This dataset contains the logs collected by <strong>Unistudium</strong>, the University of Perugia elearning platform based on moodle, a open source software for learning management systems (https://moodle.org).</p> <p>The collected logs record interactions with the platform of students attending 4 courses during the time period of one semester, from 1st September to 31st December. </p> <p>The platform support blended learning, providing teaching material to courses in presence delivered at University of Perugia. Data are completely anonymized.</p> <p>The dataset has been developed as part of the research activity of the scientific research group <strong>KITLab - Knowledge and Information Technology Laboratory</strong> (https://www.dmi.unipg.it/kitlab/). KIT Lab is member of the CLAIRE Network: The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE)</p>
3D Archaeological Greek Pottery: MHNC-UP-020060, Gnathian cup-skyphos, Laurel Spray Group
<p><strong>This 3D dataset is related to the publication</strong>:</p> <ul> <li>Moitinho de Almeida, V. (2023). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In <em>Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal</em>. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> <p>3D processed dataset for object MHNC-UP-020060 in the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), Portugal. <strong>CC BY-NC-SA 4.0 license</strong>.</p> <p>MHNC-UP-020060 is a Gnathian cup-<em>skyphos</em>, Laurel Spray Group, dating from c. 330 BCE and unknown provenance (Rocha Pereira & Morais, 2007; Morais, 2019; Morais et al., 2021).</p> <p><strong>Aims</strong>: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery.</p> <p><strong>Data acquisition</strong>: at the MHNC-UP, with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata included in associated spreadsheet.<br><strong>Data processing</strong>: 24 scans aligned and merged. MHNC-UP-020060_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MHNC-UP-020060_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Additional metadata included in associated spreadsheet.</p> <p>Access to the MHNC-UP-020060 was granted by the MHNC-UP.</p> <p>When citing this material: please include the original inventory ID (MHNC-UP-020060) reference to the physical object.</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.