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”
SGS-LTER Long-Term Monitoring Project: Body weights of rodents captured during SGS-LTER live-trapping on the Central Plains Experimental Range, Nunn, Colorado, USA 1994 -2011, ARS Study Number 118
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83452. Body size is a fundamental biological measurement that is known to be related to an organism's physiology, life-history and ecology. Estimates of body size are also widely used in comparative evolutionary and ecological studies, including food web and diet studies that require estimates of biomass. Beginning in 1994, small mammals are live-trapped twice each year on the three grassland and three shrubland trapping webs. Individuals are weighed (to nearest 0.5 g using a Pesola spring scale) when first captured during a given trapping session but not upon recapture during the same session. Weights are calculated by subtracting the weight of an empty capture (ziploc) bag from the weight of animal in the bag. Individuals are classified into age classes (adult, subadult, juvenile) in the field based on a combination of size and pelage characteristics. This dataset gives means, standard deviations, minimum and maximum values for body weight, in grams, of small mammals captured between September 1994 and September 2008. All sites and sampling periods were combined. Most individuals (~93%) were classified as new captures, although a few individuals that were captured multiple time
An accessible scheme for monitoring free-roaming cat population trends
Data and R scripts for article: "An accessible scheme for monitoring free-roaming cat population trends" published in Ecology and Evolution, Wiley.
Data and code for publication: zu Ermgassen et al., "Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector"
<p>Data and code for the publication zu Ermgassen et al., "Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector".</p> <p>This includes one data file (zdc-monitoring.rdata) and three scripts, to be run in [R]:<br> 01_PREPARE_DATA.R - Loads the data (from a user-specified location) and then produces summary data which are called in subsequent scripts.<br> 02_PLOT_FIGURES.R - Reproduces all figures in the manuscript.<br> 03_KEY_STATISTICS.R - Reproduces all statistics quoted in the manuscript, which are based on the Trase data.</p> <p>More detailed descriptions of the data contents and scripts are given in the README.md file.</p>
Carros Storage full monitoring
<p>Full data set of Carros storage system monitoring, including : SOC, P, Q, target active power, converte AC and DC Voltages, I.</p>
Lérins islands islanding system : MV distribution network monitoring data
<p>U and I measurment at Gridf Forming Unit connexion point during an islanding and when connected to the continent, and global consumption of Lérins islands.</p>
Jira Monitoring Data September 2018
<pre>This is the fourth of four datasets that our research "Comparing Static and Dynamic Weighted Software Coupling Metrics" published in <a href="https://doi.org/10.1007/978-3-030-30275-7_22">https://doi.org/10.1007/978-3-030-30275-7_22</a> is based on. This dataset contains the monitoring data from our installation of Atlassian Jira, from September 2018.</pre>
Figure 3 in Terrestrial isopods as bioindicators for environmental monitoring in olive groves and natural ecosystems
Figure 3. Activity density of isopods in olive grove management systems. Letters indicate homogeneous groups.
Data for "Characterization of composition and sources of atmospheric submicron particles in Xi'an, China during summer using an aerosol chemical speciation monitor"
<p>The dataset used for the study of Li et al. (2020). </p>
PyonAir: An open design, open source, air quality monitor for community driven particulate matter sensing.
<p>This dataset present some of the data recorded by two low-cost PM sensors, a Plantower PMS5003 and a Sensirion SPS030 located at Southampton AURN reference station on the 2nd December 2019 between 17:00 and 22:00 during a fire that occurred in the city. It also present the data from the Fidas 200, averaged every 15min also located at the AURN station.</p> <p>The file SPS_PMS_fire.csv contain the following rows:</p> <ul> <li>date</li> <li>pm25 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> <li>sensor - Serial number of the sensor</li> <li>site - name of the location of the sensor</li> </ul> <p>The file fidas_15min.csv contains the following rows:</p> <ul> <li>date</li> <li>PM2.5 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> </ul> <p> </p>
Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series
<p>This dataset considers information from clear-cut coppice forest occurred between 1999 and 2015 in three different area of Tuscany Region (Italy). </p> <p>The dataset consists in one shape file and two csv files.</p> <p>The shape file contains in 2371 spatial polygons of clear-cut occurred in the three area between 1999 and 2015. The polygons were generated using visual interpretation of the Landsat Time Series and high resolution regional ortomosaic. Photo interpreters delineated the spatial extent of each clear-cut and recorded the year of harvest, adopting a minimum mapping unit of 0.1 hectares. The database associated with shape file report information related with area extent of clear-cut, year of clear-cut and the forest types that is associated with the clear-cut derived by the Tucant Regional Forest Inventory (RFI) data that give information on dominant species. </p> <p>The .csv files reported the temporal characteristic of clear-cut based on Landsat Time Series data and based on Airborne Laser Scanner (ALS) Canopy Height Model (CHM), in order to describe the temporal trend of clear-cut based on remote sensing data.</p>
Supplementary Material for "Updating and Extending an UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases"
<p>This is the supplementary material for our publication "Updating and Extending an UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases".</p> <p> </p> <p><strong>For a detailed description of this material, please see the "Notes on the Supporting Material".</strong></p> <p> </p> <p>Naturally, this publication builds on previous work. The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publication (10.3390/mps2030060, 10.5281/zenodo.3333469). Applications of this methodology are described in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858).</p> <p>I wish to thank all authors of this publication for their contribution, using the method and generously sharing your experience and your data with me. This helped us develop robust protocols, learn about the potential and limits of the method and ultimately made this update article possible. While I have been the primary user of the method, your applications, questions and problems have spurred us to think outside of the box and find solutions that helped us expand our repertoire of "unmixable" reactions. This Supplementary Information contains some of our shared data and I thank each of you for contributing to this work.</p> <p> </p>
Air Quality Monitoring and People Counting
<p>This dataset contains data on air quality monitoring and people counter of an office room at the University of Messina. Data is stored and labeled with the corresponding people number.</p> <p>A single record is composed by;</p> <p><strong>Date</strong> s is a timestamp; </p> <p><strong>Id</strong> is an Identifier number; </p> <p><strong>Pm1</strong>, <strong>Pm 2.5</strong> and <strong>Pm10</strong> are the dust sensors;</p> <p><strong>Temp</strong> is the temperature;</p> <p><strong>Hum</strong> is humidity;</p> <p><strong>Press</strong> is the atmospheric pressure; </p> <p><strong>CO</strong> and <strong>CO2</strong> are the concentrations;</p> <p><strong>Label </strong>is the number of people. </p> <p> </p> <p> </p>
Admiralty Bay monitoring 2019
<p>Data are described and supplemented the manuscript submitted to review to Journal of Geophysical Research: Oceans entitled: Variability and interrelation of quality parameters of surface waters in five tidewater glacier coves of King George Island, Antarctica by: Osińska, Bialik and Wójcik.</p>
Mobile and stationary air pollution measurements around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland
<p>Mobile and stationary measurements of air pollution around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland. The datasets are used in evaluating high-resolution air quality simulations conducted with the PALM model system 6.0. The dataset contains:</p> <ul> <li>drone_data: measurements of vertical profiles of lung-deposited surface area (LDSA) conducted using a drone in summer and winter 2017</li> <li>kumpula_airquality_data: <ul> <li>airquality: basic air quality observations from the SMEAR III station</li> <li>dmps: aerosol size distribution observations using DMPS from the SMEAR III station</li> </ul> </li> <li>met_data: <ul> <li>kivenlahti_mast_DDMMYYYY.txt: Kivenlahti mast observations</li> <li>Meteorology_YYMM_10.txt: SMEAR III observations</li> </ul> </li> <li>sniffer_data/long/[timeofday]YYYYMMDD.dat: horizontal distribution of air pollutants measured by the mobile laboratory Sniffer</li> <li>supersite_data: measurements from the air quality monitoring station on Mäkelänkatu <ul> <li>AQdata: air quality observations</li> <li>DMPS: aerosol size distribution observations using DMPS</li> <li>ACSM: aerosol chemical composition measurements using ACSM</li> </ul> </li> </ul> <p> </p>
Monitoring of cow location in barn by an open source low cost low energy Bluetooth tag system
<p>Supplementary materials for: Bloch, V., Pastell, M., 2020. Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System. Sensors 20, 3841. <a href="https://doi.org/10.3390/s20143841">https://doi.org/10.3390/s20143841</a></p>
Monitoring spatio-temporal snow depth in the Chilean Andes using spaceborne tri-stereo photogrammetry
<p>Data provided for submitted work:</p> <p>Thomas E. Shaw, César Deschamps-Berger, Simon Gascoin, James McPhee</p> <p>Monitoring spatial and temporal differences in Andean snow depth <br> derived from satellite tri-stereo photogrammetry</p> <p>---------</p> <p><strong>Contents:</strong></p> <p> <strong>'SDmap_2017_3m.tif</strong>' = Filtered DEM difference for September 4th 2017.<br> <strong>'SDmap_2019_3m.tif'</strong> = Filtered DEM difference for September 2nd 2019.<br> <strong>'SDmap_gapfilled_2017_3m.tif'</strong> = Filtered DEM difference for September 4th 2017, gap-filled by random forest model. Random forest model initialised by 100 runs, using all available snow depths in 2017 and topographic indices as predictors (Shaw et al., 2020).<br> <strong>'SDmap_gapfilled_2019_3m.tif'</strong> = As above, but for the filtered DEM difference of September 2nd 2019. <br> <strong> 'Slope_3m.tif'</strong> = Slope angle (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'TPI_3m.tif' </strong>= Topographic position index (TPI - Revuelto et al., 2014) derived from the snow-free DEM (6th January 2018) based upon a 60 m search distance.<br> <strong>'Aspect_3m.tif' </strong>= Aspect (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'Exposure_SX_3m.tif'</strong> = Exposure parameter (SX) based upon Winstral et al. (2002) derived from the snow-free DEM (6th January 2018) and dominant ERA5 10 m wind direction for 2017 and 2019 winter average (45°).<br> <strong>'SkyViewFraction_3m.tif' </strong>= Sky view fraction derived from the snow-free DEM (6th January 2018).</p> <p>---------</p> <p><strong>NOTE:</strong></p> <p>Products of the Pléiades DEM processing are provided here, though publication of the raw Pléiades DEMs are restricted by the regulations of the CNES agreement for grant PNTS‐2018‐ 4.</p> <p>---------</p> <p><strong>Cited works:</strong></p> <p><strong>Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., & Vicente-Serrano, S. M. (2014)</strong>. Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: Intra- and inter-annual persistence. The Cryosphere, 8(5), 1989–2006. https://doi.org/10.5194/tc-8-1989-2014<br> <strong>Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., & McPhee, J. (2020)</strong>. Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing Water Resources Research. Water Resources Research, 56, 1–23. https://doi.org/10.1029/2019WR024880<br> <strong>Winstral, A., Elder, K., & Davis, R. E. (2002)</strong>. Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters. Journal of Hydrometeorology, 3(5), 524–538. https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2</p>
Asynchronous event-based clustering and tracking for intrusion monitoring in UAS
<p>This dataset describes a collection of rosbag files for event-based intruder monitoring using UAS. A DAVIS346 camera was mounted over a DJI Flamewheel F550 Drone, and an onboard computer recorded the sensor information from the event camera. Each dataset includes events, frames, and IMU measurements. The monitoring scenes were recorded outdoors at the School of Engineering of the University of Seville. In each dataset, an intruder moves and hides from the field of view of the camera simulating a scape-intrusion situation. A total of four monitoring setting were recorded:</p> <p><strong>Daylight monitoring:</strong> A daylight scene for intruder monitoring. An intruder runs and hides behind the objects of the scene to evade the camera field of view.<br> <br> <strong>Night light monitoring:</strong> A monitoring scene during the night without the presence of any artificial light. The low light condition increases the difficulty of monitoring task due to the increment of noisy events.<br> <br> <strong>Multi-target:</strong> An experiment with a suspect and a chaser drone moving in the monitoring area. The drone follows the suspect by simulating a pursuit operation.<br> <br> <strong>Monitoring under illumination changes:</strong> A night scene where the lighting conditions changes by the movement of artificial lights in the scene.</p>
Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data
<p>This storage contains the training data for neural networks proposed in a manuscript 'Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data'. The data consists of output from reservoir simulations of a small-scale CO2 injection at CO2CRC Otway Project Stage 2C (Victoria, Australia). The output is presented as a set of images, where each pixel in a portable network graphics is a plume thickness for a particular injection scenario at a particular day after the injection has commenced. The format is unsigned integer 16-bit. The data set contains images of two major types:</p> <p>1. REALISTIC: plumes are obtained from reservoir simulations in a complex geological model that was calibrated on an extensive set of geophysical surveys. File naming follows this convention 'plume_thick_real_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p> <p>2. VANILLA: plumes are obtained from reservoir simulations in a simple model of a reservoir that reflects only few typical features of the Otway injection interval. 'plume_thick_vanilla_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.</p>
Agricultural Monitoring in Vojvodina, Serbia
<p>This dataset had been collected during the LandSense agricultural demonstration pilot, which focuses on leveraging the power of Earth Observation (EO) systems and advanced crowdsourcing techniques to deliver value added services to European farmers and public authorities in the agricultural sector. Pilot activities, led by INOSENS, were localized in Vojvodina province of Serbia and were implemented over two phases. During the Phase 1 campaign (Nov, 2017-Sep, 2018), agricultural high school students and master students from University of Novi Sad were actively involved in data collection. During the Phase 2 campaign (Feb-Oct, 2019), a group of individual farmers participated in the collecting of agricultural in-situ data. The mobile and web-based <a href="https://landsense.inosens.rs/">CropSupport application</a>, developed by INOSENS was the primary tool for in-situ data collection related to crop type and farm management. Besides the functionalities for intuitive in-situ data collection, the application allows users to access (free of charge) information related to their land, such as weather forecast for the micro locations of their parcels, the LandSense Change Detection Service as well as processed satellite images that contain information on potential stress of crops due to exposure to pests, plant diseases, water scarcity or nutrient elements in the soil (e.g. NDVI, vegetation and moisture indexes). The datasets show the delineated parcels and crowdsourced in-situ photographs for crop type classification.</p> <p><strong>Contributors</strong>:<br> 1. High School students from (i) Srednja poljoprivredno-prehrambena škola “Stevan Petrović Brile”, Ruma; (ii) Poljoprivredna škola Bač, Bač.<br> 2. Master Students from the University of Novi Sad<br> 3. Farmers (“Club of farmers Selenca”, “Center for Organic production Selenca”and individual farmers from following villages: Srbobran, Zabalj)</p> <p>Associated files: CropSupport Attributes.csv, CropSupport images.csv, CropSupport images.geojson, CropSupport parcels.csv, CropSupport parcels.geojson</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://inosens.rs/">INOSENS</a>, and <a href="https://www.sinergise.com/">SINERGISE</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
hmchou/time-series: lake monitoring data from YYL & CFL
<p>raw data; June 2019 <a href="https://github.com/hmchou/time-series/files/4979016/201906.zip">201906.zip</a></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.