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opencc-by-4.0Dec 2023View details →

PROCRAFT Final Meeting - Collaborative work between institutions and volunteers by Marie Grima - National Museum of Flight

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opencc-by-4.0Dec 2023View details →

PROCRAFT Final Meeting - Case study: conservation of the Dornier 17 wreck (collaboration between institutes and volunteers) by Darren Priday, RAF Museum

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opencc-by-4.0Nov 2023View details →

Cine and real-time free-breathing CMR at rest and under exercise stress of healthy volunteers

The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany). Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress. The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES). The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502). To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. For inter-observer variability, manually corrected contours for the derivation of the clinical parameters were created for the first five volunteers by a second reader with experience in cardiac segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice. Image data is provided in a file format used by the BART toolbox.  DOI:10.5281/zenodo.7110562

opencc-by-4.0Jan 2022View details →

Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>.  When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are:  <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep).  The dataset can be read in using e.g. Pandas:  ``` import pandas as pd dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db') ``` Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset.  Description of series in dataframe <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul>   ``` For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo. For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo.

opencc-by-4.0Feb 2022View details →

Volunteer classifications of images from the Cropland Capture game

Each entry represents a single classification of a single image by a volunteer rater. The dataset contains six columns: imgid:     The unique identifier for each image used in the Cropland Capture campaign userid:    The unique identifier for each volunteer in the Cropland Capture campaign rating:    The answer provided; can be only one of the following:          1: yes cropland          2: no cropland          0: maybe  date:        Timestamp of the rating ratingid:    The unique identifier of the rating (this is different for each data row) platform:    What interface did the volunteer use to provide this rating?          1: iPhone5          2: iPhone, other models          3: iPad          4: Browser           >100: Android; different numbers indicate the screen size in pixels  For more information, please see the following publications: Salk, CF, T Sturn, L See, S Fritz (2017).  Limitations of majority agreement in crowdsourced image interpretation. Transactions in GIS, 21: 207–223. Salk, CF, T Sturn, L See, S  Fritz (2016).  Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task.  Remote Sensing, 8: 744. Salk, CF, T Sturn, L See, S Fritz and C Perger (2016).  Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game.  International Journal of Digital Earth, 9(4): 410-426.

opencc-by-4.0Mar 2022View details →

Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers

The metabolomic / lipidomic datasets used in the manuscript draft entiteld:  "Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication" by Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse Data is untransformed and missing data were imputated. All values are in µmol/L.

opencc-by-4.0May 2022View details →

Influence of conspiracy theories and distrust of community health volunteers on adherence to COVID-19 guidelines and vaccine uptake in Kenya

This cross-sectional study collected data between 25 May –27 June 2021 n=447. It involved all registered community health volunteers (CHVs) who had participated in the COVID-19 vaccine hesitancy study. This data was collected as part of an Epidemic Ethics/WHO initiative that FCDO/Wellcome Grant 214711/Z/18/Z has supported. WHO’s specific grant number was 2020/1077878-0). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. No authors received a salary from the funders.

opencc-by-4.0Jun 2022View details →

Assessing the impacts of conservation volunteering on participant wellbeing: a review protocol

Original search titles for the review :  Assessing the impacts of conservation volunteering on participant wellbeing: a review protocol

opencc-by-4.0Sep 2022View details →

Biodiversity data collected by volunteers from NaturTjek 2015-2020

This .csv file contains all observation made by volunteers between 2015 and 2020 as part of the Danish large-scale citizen science project: "NaturTjek" or "Biodiversitet Nu". Each line represents an observation made by a volunteer profile and contains information on the species, the time, the location and (anonymized) informaiton on the recorder (i.e. and ID number). Columns containing 1) the username, 2) the last name and 3) the birth year of the volunteers have been removed. In addition to the information collected as part of the original species observation, the file also contains information on selected geo-spatial attributed connected to the spatial location of the observation and associated post-collection.

Figure 7 in Interference and management of herbicide-resistant crop volunteers

Figure 7. Individual rows of weedy rice accessions or cultivated rice cultivars 8 d following a post-flood application of benzobicyclon at 371 g ai ha−1. Healthy rows are cultivated rice or resistant weedy rice accessions, whereas chlorotic rows are benzobicyclon-sensitive weedy rice accessions.

Figure 9 in Interference and management of herbicide-resistant crop volunteers

Figure 9. Field-scale evaluation of imidazolinone-resistant (ClearfieldṜ) wheat compared with non–herbicide resistant wheat (including volunteers the following year) in Saskatchewan, Canada, in the early 2000s (adapted from Beckie et al. 2011).

Figure 4 in Interference and management of herbicide-resistant crop volunteers

Figure 4. Symptoms of (A) glufosinate on glyphosate-resistant volunteer corn in glufosinate-resistant soybean and (B) sethoxydim on glyphosate/glufosinate-resistant volunteer corn in dicamba/glyphosate-resistant soybean.

Figure 2 in Interference and management of herbicide-resistant crop volunteers

Figure 2. Soybean after corn is a typical rotation in the midwestern United States. If not controlled, volunteer corn is a problem weed in soybean fields.

Figure 1 in Interference and management of herbicide-resistant crop volunteers

Figure 1. Glyphosate- and glufosinate-resistant canola volunteers in adjacent fields in Saskatchewan, Canada, due to bidirectional pollen-mediated gene flow the previous year.

Figure 3 in Interference and management of herbicide-resistant crop volunteers

Figure 3. Volunteer corn in a cornfield in Nebraska. Highly productive soils and easy access to irrigation have encouraged growers to adopt a corn-on-corn cropping system in south-central Nebraska that results in corn volunteers.

opencc-by-4.0Jul 2017View details →

Figure 9. Sometimes many volunteers are necessary for a in Stakeholder contributions to conservation of threatened Northern Pine Snakes (Pituophis melanoleucus, Daudin, 1803) in the New Jersey Pine Barrens as a case study

Figure 9. Sometimes many volunteers are necessary for a project, either digging up a hibernation site (Fig 9a), clearing open areas for sun penetration, or digging up an old septic line to prevent collapses and injuries to snakes.

Figure 5 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 5. Challenges (n = 56) faced by individual volunteers in the management of invasive alien plant management in Western Cape, South Africa.

Figure 4 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 4. Reasons for initial engagement (n = 71) in volunteering and the current motivations (n = 86) for volunteers to be involved in the management of invasive alien plant species in Western Cape, South Africa.

Figure 2 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 2. Motivations (n = 35) for forming volunteer groups that remove alien invasive plants in Western Cape, South Africa.

Figure 1 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 1. Identified volunteer groups (52) in Western Cape of South Africa. Groups that participated in the survey (26) are indicated by circles that also show group sizes (individual members per group). Groups that did not participate in the survey are indicated by blue circles. The green area on the map represents the fynbos biome.