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5,225 results for “volunteers”
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Journey North - Oriole observations by volunteer community scientists across Central and North America (1997-2020)
This data package contains oriole migration data consisting of 14,476 total observational reports from 1997 - 2020 across North and Central America. These data were collected by 7,331 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Oriole Project is an ongoing study of oriole phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of oriole species throughout the winter months in the United States. Focal species are the Baltimore Oriole (Icterus galbula), Bullock’s Oriole (Icterus bullockii), and Orchard Oriole (Icterus spurius fuertesi). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Oriole Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Common Loon and Ice-Out observations by volunteer community scientists across North America (1997-2020)
This data package contains Common Loon migration and ice melt data consisting of 9,800 total observational reports from 1997 - 2020 across North America. These data were collected by 1,437 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Loon and Ice-Out Project is an ongoing study of loon and ice melt phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates of Common Loons (Gavia immer) and ice that has melted from bodies of water in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Common Loon and Ice-Out Project Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Gray Whale observations by volunteer community scientists across the Eastern Pacific Ocean (1997-2020)
This data package contains Gray Whale migration data consisting of 1,546 total observational reports from 1997 - 2020 across the Eastern Pacific Ocean. These data were collected by 163 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Gray Whale Project is a study of Gray Whale phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked the migration of Gray Whales (Eschrichtius robustus) through the Eastern Pacific Ocean. Observers also provide estimates of the number of whales sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Gray Whale Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Volunteer kinematics from emergency lateral maneuvers, taken from van Rooij et al, 2013
<p>This data was extracted by the author as part of the OSCCAR project from the publication: </p> <p>Van Rooij, L., Elrofai, H., Philippens, M. M. G. M., & Daanen, H. A. M. (2013). Volunteer kinematics and reaction in lateral emergency maneuver tests. Stapp car crash journal, 57, 313 </p> <p>It is being made available to aid in the validation of Human Body Models.</p> <p>If you use this data, please cote the original paper.</p> <p>A model is available on request from the author of this upload, at Siemens Industry Software Netherlands BV. Modelling information regarding the experimental setup is also available in the public OSCCAR deliverable D3.2.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. The usernames have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>).</p> <ul> <li><code>corresponding_calculations.html</code> <ul> <li><em>Quarto-notebook to view in browser</em></li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li><em>Quarto-notebook to view in RStudio</em></li> </ul> </li> <li><strong>assets</strong> <ul> <li><strong>data</strong> <ul> <li><strong>annotations</strong> <ul> <li><code>annotations.csv</code> <ul> <li><em>List of annotations made per day for each of the analysed projects</em></li> </ul> </li> </ul> </li> <li><strong>comments</strong> <ul> <li><code>comments.csv </code> <ul> <li><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></li> </ul> </li> </ul> </li> <li><strong>rolechanges</strong> <ul> <li><code>478_rolechanges.csv</code> <ul> <li><em>List of roles per user to determine number of role changes </em></li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>totalnetworkdata</strong> <ul> <li><strong>Edges</strong> <ul> <li><code>478_edges.csv</code> <ul> <li><em>Network data (edge set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>478_nodes.csv</code> <ul> <li><em>Network data (node set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_nodes.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></li> <li><strong>478</strong> <ul> <li><strong>Edges</strong> <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p><strong>1104</strong> </p> <ul> <li> <p><strong>Edges</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><strong>Nodes</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p>...</p> </li> </ul> </li> </ul> </li> <li><strong>scripts</strong> <ul> <li><code>datavizfuncs.R</code> <ul> <li><em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li><em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>corresponding_calculations_files</strong> <ul> <li>f<em>iles for the html/qmd view in the browser/RStudio</em></li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure</p>
Magnetic resonance spectroscopy data acquired in tinnitus subjects and healthy volunteers using PRESS sequence
<p>This dataset contains raw free induction decay (FID) signals collected during 1H magnetic resonance spectroscopy (MRS) study in 52 individuals with tinnitus (24 with unilateral and 28 with bilateral tinnitus) and 25 healthy volunteers (described in detail in a separate article doi:10.1038/s41598-023-45024-3).</p><p>Data acquisition was performed using 3T Siemens Prisma Fit scanner with a 20-channel receiver head-coil. A single voxel spectroscopy (SVS) PRESS (Point-Resolved Spectroscopy Sequence) sequence was applied for collection of MRS data, using standard Siemens water suppression (water saturation, 50 Hz bandwidth) and no lipid suppression. MRS data was collected from four cubic 3.75 cm3 (1.5 cm x 1.5 cm x 1.5 cm) regions-of-interest in the brain, placed in the left temporal lobe, right temporal lobe, left frontal lobe, and right frontal lobe. The MRS sequence parameters were: TR (time of repetition) = 2000 ms, TE (time of echo) = 40 ms, TA (time of acquisition) = 4 min 26 s, 128 averages with 1024 time points and 1200 Hz bandwidth.</p><p>MRS data is stored in RDA file format, developed by Siemens (see doi:10.1002/nbm.4257, Table 1). Each RDA file contains a text header (which can be viewed using a standard notepad application) and binary FID signal under the header. Data can be imported for analysis using several open-source packages (tested with FID-A doi:10.1002/mrm.26091 and spant doi:10.21105/joss.03646). </p><p>Naming scheme of files is as follows:</p><p><participant ID>_<hemisphere: L or R>_<region: F (frontal) or T (temporal)>.rda</p><p>For example: <i>001_L_F.rda</i> is data from participant 001 collected from a voxel placed in a ROI in the left frontal lobe.</p><p>In order to allow replication of the results from the original article, we also added information about the group of each of the subjects. This information is stored in a TSV file containing two columns: <i>participant_ID</i> and<i> group</i> (C – control, TU – unilateral tinnitus, TB – bilateral tinnitus).</p><p>Aside from replication of our results this dataset may be used e.g. for testing of different MRS data processing pipelines.</p>
Journey North - Red-winged blackbird observations by volunteer community scientists across Central and North America (1999-2020)
This data package contains Red-winged Blackbird migration data consisting of 9,352 total observational reports from 1999 - 2020 across North and Central America. These data were collected by 4,647 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Red-winged Blackbird Project is an ongoing study of Red-winged Blackbird phenology conducted at broad spatial and temporal scales. Since 1999, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of Red-winged blackbird species throughout the winter months in the United States. The focal species is the Red-winged Blackbird (Agelaius phoeniceus). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Red-winged Blackbird Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - American Robin observations by volunteer community scientists across Central and North America (1996-2020)
This data package contains American Robin migration data consisting of 39,260 total observational reports from 1996 - 2020 across North and Central America. These data were collected by 17,619 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North American Robin Project is an ongoing study of American Robin phenology conducted at broad spatial and temporal scales. Since 1996, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of American Robin species throughout the winter months in the United States. The focal species is the American Robin (Turdus migratorius). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North American Robin Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Barn Swallow observations by volunteer community scientists across Central and North America (2000-2020)
This data package contains Barn Swallow migration data consisting of 3,836 total observational reports from 2000 - 2020 across North America and Central America. These data were collected by 1,320 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Barn Swallow Project is an ongoing study of Barn Swallow phenology conducted at broad spatial and temporal scales. Since 2000, community scientists have tracked first arrival dates of Barn Swallows (Hirundo rustica) in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Barn Swallow Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Tulip observations by volunteer community scientists across North America (1996-2020)
This data package contains tulip phenology data consisting of 21,148 total observational reports from 1996 - 2020 across North America. These data were collected by 6,645 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Tulip Test Garden Project is an ongoing study of tulip phenology conducted at broad spatial and temporal scales. Since 1996, community scientists have tracked planting, emergence, and blooming of tulips (Tulipa L.) in the United States. Most observations should be of the Red Emperor Tulip, but not all observations can be validated as this species. Researchers are encouraged to read observer comments to confirm tulip species. Observers also provide estimates of the number of tulips sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate broad phenological information. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Tulip Test Garden Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
<p>This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS.</p> <p>- gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns.</p> <p>- gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses.</p> <p>- gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases;<strong> the expected vote fractions (mean posteriors)</strong>. Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question.</p> <p>We include a schema document, schema.md, to define the column names in each catalogue.</p> <p>We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester <a href="https://docs.google.com/document/d/1YgpnxiSJ7ffOW6FY8pX0pw93LTu8rLIdPL2PYhxW1fo/edit?usp=sharing">here</a>.</p> <p>The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]).</p> <p>You may also be interested in the <a href="https://github.com/mwalmsley/zoobot">github repository</a> which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights).</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p>History</p> <p>v0.0.1 (submission) provides the catalog files.</p> <p>v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Journey North - Hummingbird observations by volunteer community scientists across Central and North America (1996-2020)
This data package contains hummingbird migration data (1996 - 2020) across North and Central America collected by 30,703 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Hummingbird Project is an ongoing study of hummingbird phenology conducted at broad spatial and temporal scales. Since 1996, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of hummingbird species throughout the winter months in the United States. Focal species are the Ruby-throated Hummingbird (Archilochus colubris) and Rufous Hummingbird. However, observational data is also available for Broad-tailed Hummingbird (Selasphorus platycercus), Black-chinned Hummingbird (Archilochus alexandri), Calliope Hummingbird (Selasphorus calliope), Costa's Hummingbird (Calypte costae), Anna’s Hummingbird (Calypte anna), Allen’s Hummingbird (Selasphours sasin). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence, not abundance. Additional contextual information is provided as text in the comments field of the dataset. The Journey North Hummingbird Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Monarch Butterfly and Milkweed observations by volunteer community scientists across Central and North America (1996-2020)
This data package contains monarch migration data (1996 - 2020) across North America collected by 42,518 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Monarch and Milkweed project is an ongoing study of monarch migration phenology conducted at broad spatial and temporal scales. Since 1996, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration, fall roosts, and peak migration events. Community scientists have also tracked the first emergence of milkweed in the spring and the presence of milkweed across the landscape during the summer, fall-winter months. The focal species is the Monarch Butterfly, Danaus plexippus. Community scientists indicate when known milkweed species in submitted comments. Journey North data is observational and often opportunistic in nature. Observers do provide estimates of the number of adult monarch butterflies sighted during an observation. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day; do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence, not abundance. Additional contextual information is provided as text in the comments field of the dataset. The Journey North Monarch and Milkweed project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Seven Tesla MRI of the pelvis of 11 volunteers
<p>MRI dataset of the pelvis of 11 young volunteers.</p> <p>Each volunteer set contains two anonymized dicom 3D image series:</p> <p>1. Anatomical overview of the pelvic area with a 3D dataset of 256 images, 'lipid excitation'</p> <p>2. Multi gradiënt echo dataset of 5 gradiënt echoes, 3D datset of 256 images per echo (1280 images), 'water excitation'.</p> <p>See the following PlosOne publication for details on acquisition parameters:</p> <p><strong>Magnetic resonance imaging at ultra-high magnetic field strength: an in vivo assessment of number, size and distribution of pelvic lymph nodes</strong></p> <p>Ansje S. Fortuin, Bart W.J. Philips, Marloes M.G. van der Leest, Mark E. Ladd, Stephan Orzada, Marnix C. Maas, Tom W.J. Scheenen</p>
Twenty four hour continuous tympanic temperature recordings in healthy volunteers and patients presented with Undifferentiated fever
<p>Twenty-four-hour continuous tympanic temperature recordings were obtained using high-accuracy tympanic probes placed at the auditory canal. Measurements were recorded at one-minute intervals over a 24-hour period, yielding 1,440 data points per subject and enabling high-resolution temporal profiling of body temperature.</p> <p>In Phase I, a total of 100 healthy adult volunteers were recruited to establish baseline circadian thermoregulatory patterns. These recordings were stratified by gender to examine physiological variability among individuals without fever.</p> <p>In Phase II, 184 adult patients presenting with undifferentiated fever of seven or more days’ duration were enrolled. Based on clinical examination and laboratory confirmation, the temperature profiles were analysed across a range of conditions, including tuberculosis, non-tubercular bacterial infections, dengue fever, malaria, leptospirosis, pyogenic sepsis, thyroiditis, malignancies, and non-infectious inflammatory diseases. This dual-phase dataset facilitated a comparative analysis between normative and pathological thermoregulatory patterns, supporting the development of classification models for diagnostic differentiation.</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.