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”
Caribbean Roseate Tern nest survival and monitoring data
Determining how site characteristics influence reproductive success can help guide conservation planning for declining wildlife populations. For colonially-breeding seabirds, nest survival and predation risk can be influenced by both colony and nest site characteristics such as colony size, nest density, and nest location. We evaluated reproductive success of a declining population of Roseate Terns (<i>Sterna dougallii</i>) in the U.S. and British Virgin Islands to identify primary causes of nest failure and investigate the influence of colony and nest site covariates on nest survival. In addition, we measured the influence of colony characteristics on chick provisioning to determine if resource competition in larger colonies affected foraging rates. Monitoring was conducted in 2017–18 via motion-triggered cameras, which enable 24-hour monitoring with minimal nest disturbance. Nest survival was defined by both hatch success and early chick success, defined as a chick surviving from hatching through the 4<sup>th</sup> post-hatch day. We monitored 118 nests in 6 colonies over the 2 breeding seasons. We found that predation was the primary cause of nest failure. Both hatch and nest success increased with colony size, and neither nest survival nor predation probability were influenced by individual nest site characteristics. Provisioning rates were comparable among colony sites and did not vary with colony size. Our results suggest that directing management efforts toward enhancing colony size, rather than focusing on nest site characteristics, can be beneficial for tropical seabird conservation.
Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery
<p>Sample video showing the 'ischemia index' computed with a deep-learning model trained on multispectral images recorded during partial nephrectomy before a clamp is applied to the renal artery (perfused) and after clamping the artery (ischemic).</p>
Raw data for "A-Proof-of -Concept: In vivo Volatile Organic Compounds Secretion Monitoring as a Tool for Prediction of Natural Pest Control Ability"
<p>This upload refers to work entitled "A-Proof-of -Concept: In vivo Volatile Organic Compounds Secretion Monitoring as a Tool for Prediction of Natural Pest Control Ability" and contains the unprocessed data obtained during experiments perform. </p>
A novel UPLC-MS metabolomic analysis-based strategy to monitor the course and extent of iPSC differentiation to hepatocytes
<p>ms2 raw data, peak tables generated in Quantitative Analysis Software from Agilent and Matlab functions for QC-SVRC, data clean-up and analysis for the publication with title "Monitoring the differentiation of iPSC to hepatocytes by means of UPLC-MS metabolomics".</p>
Pyzoflex technology testing for powder flow monitoring
<p>These data represent a signal collection from the Pyzoflex(R) sensor when exposed to diverse amounts of powder driven by Argon gas flow, to be used in the laser cladding process.</p>
Supporting Documents for "The DESC Catchments: Long-term Monitoring of Inland Precambrian Shield Catchment Streamflow and Water Chemistry in Central Ontario, Canada"
<p>Supporting documents for "<strong>The DESC Catchments: Long-term Monitoring of Inland Precambrian Shield Catchment Streamflow and Water Chemistry in Central Ontario, Canada" </strong> that are otherwise not readily accessible. These include historical government reports and relevant laboratory method descriptions from the Ontario Ministry of Environment (Canada). Reports List includes a listing of the 14 documents.</p>
Monitoring DNA hybridization with Organic Electrochemical Transistors functionalized with Polydopamine
<p>Dataset of the paper "Monitoring DNA hybridization with Organic Electrochemical Transistors functionalized with Polydopamine" published in<em> Macromolecular Materials and Engineering</em>, DOI:10.1002/mame.202100880</p> <p>The data in the excel file are organized as follows:</p> <p>In <strong>Transfer_fc-target</strong> tab is reported the mean of the I<sub>DS </sub>vs V<sub>GS</sub> curves for each of the datasets, for every concentration of fc-target.</p> <p>In <strong>Transfer_pc-target</strong> tab is reported the mean of I<sub>DS </sub>vs V<sub>GS</sub> curves for each of the datasets, for every concentration of pc-target.</p> <p>In <strong>Transfer_nc-target</strong> tab is reported the mean of the I<sub>DS </sub>vs V<sub>GS</sub> curves for 0 and 100 nM nc-target.</p> <p>In the <strong>CV</strong> tab, we show the potential vs Ag/AgCl and the current of the Cyclic Voltammetry of every gate functionalization step, shown in figure 1c of the manuscript.</p>
Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809ma.s.l.) to encourage adaptation by mountaineers
<p>The file Mourey et al._NHESS_2021_128.xlsx gathers the data used in the article "Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809ma.s.l.) to encourage adaptation by mountaineers" at an hourly time scale. </p>
Thyolo alethe (Chamaetylas choloensis) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 10 hours of audio that contained calls of the vulnerable Thyolo Alethe (Chamaetylas choloensis). The audio data was collected in the Mount Mulanje Biosphere Reserve, Malawi using 10 Audiomoths. The sampling rate was set to 32,000Hz and the recordings were obtained over five days in November 2020. A larger dataset exists.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Pin-tailed whydah (Vidua macroura) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 6 hours of audio that contained calls of the pin-tailed whydah (Vidua macroura). The audio data was collected in the Intaka Island Nature Reserve in Cape Town, South Africa using 1 Audiomoth. The sampling rate was set to 48,000Hz and the recordings were obtained over four days in January 2021. A larger dataset exists.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Black-and-white ruffed lemur (Varecia variegata) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 60 hours of audio that contained calls of the critically endangered Black-and-white ruffed lemur (Varecia variegata). The audio data was collected in a sub-humid rainforest site (Mangevo) in the southeast of Ranomafana National Park in Madagascar using 2 Swift recorders (Cornell Center for Conservation Bioacoustics). The sampling rate was set to 48,000Hz and the recordings were collected intermittently between May 2019 and November 2020. A larger dataset exists and further recordings will be released.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Hainan gibbons (Nomascus hainanus) calls for passive acoustic monitoring
<p><em>This dataset extends an existing one (10.5281/zenodo.3991714).</em></p> <p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 10 hours of audio that contained calls of the critically endangered Hainan gibbons (Nomascus hainanus). The audio data was collected in the Bawangling National Nature Reserve, Malawi using 8 Song Meter SM3 recorders. The sampling rate was set to 9,600Hz and the recordings were collected between March to August 2016.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''gibbon" (presence class) or "no-gibbon" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Monitoring data second-life battery at Cloverleaf
<p>Dataset consisting of monitoring data on the second-life battery system that was implemented in the context of Circusol:</p> <p>- Round Trip Efficiency<br> - State of Health evolution<br> - Typical Day V I & Vcell</p>
Monitoring data second-life battery Cloverleaf demonstrator Circusol
<p>This dataset includes monitoring data on the second-life battery system that was implemented in the Cloverleaf demonstrator in the context of Circusol.</p> <p>The following data are included:<br> - State of Health evolution<br> - Round trip Efficiency<br> - Typical day V I<br> - Typical day Vcell</p>
The mean annual groundwater-level trends observed by 452 monitoring wells in the North China Plain during 2003-2016
<p>The mean annual groundwater-level (GWL) trends observed by 452 monitoring wells (341 unconfined and 111 confined) in the North China Plain (NCP) during 2003-2016. The monthly GWL data were obtained from the Chinese groundwater management authority and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region and each well follows a quality control with at least 60% of temporal coverage of the study period having GWL data records.</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Albania
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Institute of Food Safety and Veterinary</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Estonia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Veterinary and Food Board</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Finland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Finnish Food Authority</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Bulgaria
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Risk Assessment Center on Food Chain</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Greece
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Ministry of Rural Development and Food</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.