Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8,460
datasets available to search
ShareScore release 0.9.0
Dataset results
8,460 results for “MONITORING”
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
<p class="MsoNormal"><span>The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; <em>Myotis</em> spp., <em>Eptesicus nilssonii</em> or <em>Pipistrellus nathusii</em> and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for <em>E. nilssonii</em> and <em>Myotis </em>spp. For <em>E. nilssonii</em> and <em>Myotis</em> spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of <em>P. nathusii</em> increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring </span><span>network to be an effective and cost-efficient method for gathering b</span><span>at activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of cli</span><span>mate change.</span></p>
Datasets and codes for De Lorm et al. 2023: Optimising the automated recognition of individual animals to support population monitoring
<p>Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages.</p> <p> </p> <p>In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I<sup>3</sup>S-Pattern, and WildID. As a case study, we consider the African wild dog <em>Lycaon pictus</em>, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. </p> <p> </p> <p>The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). </p> <p> </p> <p>Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts. </p>
Boats Coastal Monitoring 2023
<p>Types and quantities of boats and marine vessels observed along the Mo'orea coastline on February 20, 2023, collected and organized by timestamp and including data on a group, observer, type of fishing vessel, number of fishers, and if vessel is navigating or anchoring. </p>
Birds Coastal Monitoring 2023
<p>The number of birds (identified by common name) observed along Mo'orea coastline transects on February 20, 2023, collected and organized by timestamp and including data on observer, group, and field notes. </p>
Coastal Armoring Coastal Monitoring 2023 (Groups 1 & 2)
<p>Types of coastal armoring (i.e. sea wall) observed along the Mo'orea coastline on February 20, 2023, collected and organized by timestamp and including data on observer, group, present perpendicular structures, distance of armoring to water, and field notes. Uploaded data is one file for group 1 and one file for group 2 (not all groups compiled). </p>
Water Outlets Coastal Monitoring 2023
<p>Types of water outlets (i.e. river, storm drain, etc.) observed along the Mo'orea coastline on February 20, 2023, collected and organized by timestamp and including data on observer, group, if outlet was flowing or non-flowing, water outlet width, and field notes. </p>
A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.
<p>A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.</p> <p>Manuel Titos (1), Ligdamis Gutiérrez (2,3), Carmen Benítez (1), Pablo Rey Devesa (2,3), Ivan Koulakov (4) and Jesús. M. Ibáñez (2,3)</p> <p><br> <strong>Institutions associated:</strong></p> <p>(1) CITIC, Department of Signal Processing, Telematic and Communications, University of Granada, 18071. Granada. Spain.<br> (2) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.<br> (3) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.<br> (4) Laboratory for Seismic Forward and Inverse Problems, Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia</p> <p><br> <strong>Acknowledgment:</strong></p> <p>This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.</p> <p>a) This work is part of the research by the Spanish FEMALE project (PID2019-106260GB-I00). <strong>FEMALE </strong>(<em>Forecasting Volcanic Eruptions Using Signal Processing and Machine Learning Techniques on Seismic Signals</em>) https://femalevolcanoes.es/</p> <p><br> b) JMI and LG were partially funded by the Spanish project PROOF-FOREVER (EUR2022.134044).</p> <p><br> <strong>Keywords:</strong></p> <p>Automatic volcanic monitoring, real-time monitoring, Artificial Intelligence, Transfer Learning, Recurrent Neural Networks, Temporal Convolutional Networks.</p> <p> </p> <p><strong>Data availability statement:</strong></p> <p>Seismic data from Bezymianny volcano (2017), Kamchatka, Russia.</p> <p> </p> <p><strong>Contents:</strong></p> <p>Seismic Data from Bezymianny volcano recorded at stations BZ01, BZ02, BZ06 and BZ10.<br> The data represent the vertical component of the seismic signal, associated to the period analyzed in the study:</p>
GMAW & WAAM Process monitoring using XARION Eta300 Ultra laser microphone
<p>Repository contains extra wide bandwidth acoustic process monitoring data of a stable and unstable gas metal arc welding (GMAW) process variant Fronius "cold metal transfer" (CMT) of G3Si1 steel wire, which is used for additive manufacturing. Additional Video acquisition of the welding process is provided.</p>
Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary
<p>This data repository contains the names and description of data files used in the “Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary” manuscript by Cashel et al (2023). These data files, formatted as .txt files, include simulated and observed data of various water quality components with time. Time is always provided as the Julian Day in the first column (left). The type of data in each file is indicated by a parameter code. Parameter codes are defined below.</p> <ul> <li>SAL = salinity (ppt)</li> <li>WT = water temperature (℃)</li> <li>PAR = photosynthetically active radiation (W/m<sup>2</sup>)</li> <li>TN = total nitrogen (mg/L as N)</li> <li>NH3 = ammonium (mg/L as N)</li> <li>NO3 = nitrate + nitrite (mg/L as N)</li> <li>TP = total phosphorus (mg/L as P)</li> <li>DIP = orthophosphate (mg/L as P)</li> <li>CBOD = carbonaceous biological oxygen demand (mg/L)</li> <li>CHL = phytoplankton as chlorophyll <em>a </em>(µg/L)</li> <li>MACRO = macroalgae biomass (gDW/m<sup>2</sup>)</li> <li>DO = dissolved oxygen (mg/L)</li> </ul> <p><strong><em>1 - Simulated Data</em></strong></p> <p>1.1 – PRE Model Data</p> <p> The primary simulated data for this study includes model output on an interval of 0.05 days. Columns two through eight contain the average concentration of each WASP Segment per each time step. Data presented in these columns from left to right are from WASP Segments 8, 9, 10, 12, 14, 16, and 17.</p> <ul> <li>WASP_SAL.txt </li> <li>WASP_WT.txt </li> <li>WASP_PAR.txt</li> <li>WASP_TN.txt</li> <li>WASP_NH3.txt</li> <li>WASP_NO3.txt</li> <li>WASP_TP.txt</li> <li>WASP_DIP.txt</li> <li>WASP_CBOD.txt</li> <li>WASP_CHL.txt</li> <li>WASP_MACRO.txt</li> <li>WASP_DO.txt</li> </ul> <p>1.2 – Macroalgae Scenario Data</p> <p> Simulated data files also include a model scenario evaluating the impact of macroalgae as a state variable. This set of model output comes from simulations with macroalgae removed as a state variable. These files have a model output of 0.05 days with time in the first column, and the second column contains the average concentration within WASP segment 17.</p> <ul> <li>MACRO_PAR.txt</li> <li>MACRO_NH3.txt</li> <li>MACRO_NO3.txt</li> <li>MACRO_DIP.txt</li> <li>MACRO_CBOD.txt</li> <li>MACRO_CHL.txt</li> </ul> <p>1.3 – Dissolved Oxygen Parameter Analysis Data</p> <p> Simulated data from model scenarios evaluating the impact of parameterization on dissolved oxygen concentrations are listed below. These model simulations have an output of 0.05 days. Rows two through ten contain average DO concentration (mg/L) for WASP Segments 9, 10, 11, 12, 13, 14, 15, 16, and 17. Files containing “1” indicate the high condition of each parameter analysis, and those with a “2” indicate the low condition.</p> <ul> <li>CBOD1_DO.txt</li> <li>CBOD2_DO.txt</li> <li>Phyto1_DO.txt</li> <li>Phyto2_DO.txt</li> <li>SOD1_DO.txt</li> <li>SOD2_DO.txt</li> </ul> <p>1.4 – Heatmap Simulations</p> <p> Simulated data for heatmaps presents average concentrations from noon of each day. The first row is time, and the following rows of two through nine have simulated data for WASP Segments 10, 11, 12, 13, 14, 15, 16, and 17.</p> <ul> <li>WASP_TN2.txt</li> <li>WASP_TP2.txt</li> <li>WASP_DO2.txt</li> <li>WASP_CHL2.txt</li> </ul> <p><strong><em>2 – Observed Data </em></strong></p> <p>2.1 – Sonde Data</p> <p> Observed sonde data is presented in .txt files from various years and location. Each data file has the first column of time, and the second column as concentration per each time step. File names begin with the site name, followed by an “S” or “B” (surface and bottom respectively, if applicable), the year, and the parameter code. Site names are: LNB (Little Narragansett Bay), Pawcatuck (Pawcatuck Point), Avondale (Avondale Marina), Greenhaven (Greenhaven Marina), WYC (Westerly Yacht Club), PR (Pawcatuck Rock), Viking (Viking Marina), and R1 (Route 1). File names per location are provided in the two tables below. Row 1 of each table has the site name, and the corresponding files are listed below.</p> <table align="center"> <tbody> <tr> <td> <p><strong>LNB</strong></p> </td> <td> <p><strong>Pawcatuck</strong></p> </td> <td> <p><strong>Avondale </strong></p> </td> <td> <p><strong>Greenhaven </strong></p> </td> </tr> <tr> <td> <p>LNB_SAL.txt</p> </td> <td> <p>Pawcatuck2018_ SAL.txt</p> </td> <td> <p>AvondaleS2019_SAL.txt</p> </td> <td> <p>GreenhavenS2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_WT.txt</p> </td> <td> <p>Pawcatuck2018_WT.txt</p> </td> <td> <p>AvondaleB2019_SAL.txt</p> </td> <td> <p>GreenhavenB2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_DO.txt</p> </td> <td> <p>Pawcatuck2018_DO.txt</p> </td> <td> <p>AvondaleS2019_WT.txt</p> </td> <td> <p>GreenhavenS2018_WT.txt</p> </td> </tr> <tr> <td> <p>LNB_CHL.txt</p> </td> <td> <p>Pawcatcuk2018_CHL.txt</p> </td> <td> <p>AvondaleB2019_WT.txt</p> </td> <td> <p>GreenhavenB2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_SAL.txt</p> </td> <td> <p>AvondaleS2019_CHL.txt</p> </td> <td> <p>GreenhavenS2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_WT.txt</p> </td> <td> <p>AvondaleB2019_CHL.txt</p> </td> <td> <p>GreenhavenB2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_DO.txt</p> </td> <td> <p>AvondaleS2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_CHL.txt</p> </td> <td> <p>AvondaleB2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_DO.txt</p> </td> <td> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>WYC</strong></p> </td> <td> <p><strong>PR</strong></p> </td> <td> <p><strong>Viking</strong></p> </td> <td> <p><strong>R1</strong></p> </td> </tr> <tr> <td> <p>WYC2018_SAL.txt</p> </td> <td> <p>PRS2018_SAL.txt</p> </td> <td> <p>Viking2018_SAL.txt</p> </td> <td> <p>R1S2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_WT.txt</p> </td> <td> <p>PRB2018_SAL.txt</p> </td> <td> <p>Viking2018_WT.txt</p> </td> <td> <p>R1B2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_CHL.txt</p> </td> <td> <p>PRS2018_WT.txt</p> </td> <td> <p>Viking2018_CHL.txt</p> </td> <td> <p>R1S2018_WT.txt</p> </td> </tr> <tr> <td> <p>WYC2018_DO.txt</p> </td> <td> <p>PRB2018_WT.txt</p> </td> <td> <p>Viking2018_DO.txt</p> </td> <td> <p>R1B2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2018_DO.txt</p> </td> <td> <p>Viking2019_SAL.txt</p> </td> <td> <p>R1S2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2018_DO.txt</p> </td> <td> <p>Viking2019_WT.txt</p> </td> <td> <p>R1B2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_SAL.txt</p> </td> <td> <p>Viking2019_CHL.txt</p> </td> <td> <p>R1S2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_SALtxt</p> </td> <td> <p>Viking2019_DO.txt</p> </td> <td> <p>R1B2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_WT.txt</p> </td> <td> <p>Viking2020_SAL.txt</p> </td> <td> <p>R1S2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_WT.txt</p> </td> <td> <p>Viking2020_WT.txt</p> </td> <td> <p>R1B2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_DO.txt</p> </td> <td> <p>Viking2020_CHL.txt</p> </td> <td> <p>R1S2020_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_DO.txt</p> </td> <td> <p>Viking2020_DO.txt</p> </td> <td> <p>R1B2020_DO.txt</p> </td> </tr> </tbody> </table>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
National Open Access Monitor Survey: Defining Requirements: Responses Dataset
<p>This dataset contains the response data from the ‘National Open Access Monitor Survey: Defining Requirements’ which was carried out between February 2-28th 2023 under the National Open Access Monitor Project: <a href="https://doi.org/10.5281/zenodo.7588787">https://doi.org/10.5281/zenodo.7588787</a></p> <p>To note: </p> <ul> <li>Email addresses have been redacted. </li> <li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor 1, Research Funding Organisation #, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a>. </li> <li>Detail within responses by individuals who requested pseudonymisation, which might identify the individual or the organisation has been removed and marked "[redacted]".</li> <li>Responses by individuals who did not provide a signed consent form in advance of contributing to the survey were deleted immediately on receipt, as advised in the survey text: <a href="https://doi.org/10.5281/zenodo.7588787">https://doi.org/10.5281/zenodo.7588787</a>. Such responses are not included in these files.</li> </ul> <p>There are five files within in this dataset:</p> <p>- <em>Results.NationalOpenAccessMonitorSurvey.DefiningRequirements.README - </em>this PDF details the changes made to the raw data, as specified in the bullet points above and a description of the files within the dataset.</p> <p>- <em>Results.NationalOpenAccessMonitorSurvey.DefiningRequirements.Pseudonymised.100323</em> - this is the original raw data, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised and redacted.</p> <p>- <em>NationalOpenAccessMonitor.DefiningRequirements.Contributor3Government1.270223 - </em>this PDF file contains a single pseudonymised stakeholder response to the survey, obtained by the Project Manager by email and not through the Online Surveys platform.</p> <p>- <em>NationalOpenAccessMonitorSurvey.MasterChangeFile.080323 </em>- this is a change file, in csv format, which documents the changes which participants requested to be made to their submissions after they were received and the survey was closed.</p> <p>- <em>Results.NationalOpenAccessMonitorSurvey.DefiningRequirements.Pseudonymised.Updated.100323 - </em>this is the original raw data, pseudonymised and redacted, with the requested changes implemented. This is the file on which analysis was carried out for the purposes of the <a href="http://doi.org/10.5281/zenodo.7822367">survey outcome report</a> and the resulting National Open Access Monitor tender documents. </p> <p>------------------------</p> <p>The context for the survey files is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a></p> <p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland’s National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/</a> <a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Assessment of the nesting population demography of loggerhead turtles (Caretta caretta) in La Roche Percée: first long-term monitoring in New Caledonia.
<p><span>Population monitoring is essential to assess, manage and protect threatened species. Although the South Pacific loggerhead turtle subpopulation is classified as critically endangered by the IUCN, monitoring data are scarce. </span></p> <p><span>This study reports the results of the first long-term monitoring of the nesting population of loggerhead turtles held by Bwärä Tortues Marines on La Roche Percée beach, New Caledonia. </span><span>From 2006 to 2020, Capture Mark Recapture was used to identify nesting individuals. Time and nesting success were recorded on site.</span></p> <p><span>A total of 452 different females were observed and tagged over 14 years. The number of different nesting individuals observed each year showed a significant increase along the timeframe of the study. A remigration interval of 3.34 years was observed and the overall nesting success was 59.02%. This study also reports the inter-nesting intervals, monthly and hourly variabilities in the visits at the nesting site.</span></p> <p><span>The conservation actions led by Bwärä Tortues Marines seem to be correlated with a higher nesting success. This study provides encouraging results and highlights the need to pursue the monitoring and conservation actions implemented by Bwärä Tortues Marines. Further management recommendations are also provided.</span></p>
Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
<p><span>Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study. </span></p>
NOBEL-BOX: A Ship-Based Low-Cost Instrument for Real-Time Ocean Monitoring and Analysis
<p>This data is the measurement result obtained from the NOBEL-BOX instrument. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. This data results from measurements using fresh water and sea water mixed to see the response from NOBEL BOX. Furthermore, data was also obtained from sea measurements in Pangandaran, West Java, Indonesia. These measurements include pH, water and water temperature, dissolved oxygen, TDS, and salinity. The use of this parameter is to see the condition of the sea so that it becomes a reference in mitigating and managing the ocean.</p>
Passive acoustic monitoring applied to black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar
<p>Data accompanying the paper: <strong>"An integrated passive acoustic monitoring and deep learning pipeline applied to black-and-white ruffed lemurs (\textit{Varecia variegata}) in Ranomafana National Park, Madagascar"</strong></p> <p>Fieldwork was conducted at Mangevo (21.3833S, 47.4667E), an isolated and undisturbed forest location within Ranomafana National Park (RNP), located in southeastern Madagascar, during the period of May to July 2019. To facilitate passive acoustic monitoring, we deployed a total of two SongMeter SM4 devices (manufactured by Wildlife Acoustics) and two Swift units (provided by the Cornell Yang Center for Conservation Bioacoustics). The placement of these recorders was strategically chosen within the central regions of known subgroups, ensuring a minimum distance of 300 meters between each device. The SongMeter devices operated at a sampling rate of 48 kHz, while the Swift units operated at 32 kHz, respectively, enabling comprehensive audio data collection throughout the study period.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <ul> <li><strong>Test_Audio.zip </strong>-- contains (.wav) testing audio files</li> <li><strong>Test_Annotations.zip </strong>-- contains (.svl) manually annotated testing files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</li> <li><strong>Training_Audio_batch_x.zip -</strong>- several .zip files were created to simplify downloading. There are 10 batches, each is roughly 4GB. Each batch contains (.wav) training audio files</li> <li><strong>Training_Annotations.zip</strong> -- contains (.svl) manually annotated training files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</li> <li><strong>model_weights_tensorflow.hdf5 </strong>-- the Tensorflow model. Load the model using: model = tf.keras.models.load_model(model_filepath) note that the model expects a three channel input as explained in the research article.</li> </ul>
Transit Bus Left Side Camera Video Traffic Monitoring Dataset
<p>This dataset contains the raw data used in the research and development study reported in “Automated Traffic Surveillance Using Existing Cameras on Transit Buses”.</p> <p>This dataset consists of 11 video clips, each approximately 20 min. in duration, taken from the driver (left) side read camera of an in-service Ohio State University (OSU) Campus Area Bus Service (CABS) 40-foot transit bus running the West Campus Loop route. One set, consisting of 7 videos, was collected on a sunny day in October 2019. A second set, consisting of 3 videos, was collected during and after periods of rain and heavy rain in March 2022. Each video is accompanied by manually extracted ground truth of vehicles, and some other objects, that are in the roadway and observed by the camera.</p> <p> </p>
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
FUDGE-5G Remote Monitoring data
<p>This data was generated from the 5G Virtual Office trials. The data was collected from the vertical application, which include biosensors data, environmental data and other vertical related data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.