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.
41
datasets available to search
ShareScore release 0.9.0
Dataset results
41 results for “Habitat Monitoring”
GRTS master sample for habitat monitoring in Flanders
<p>Spatially balanced sample for the whole of Flanders and the Brussels Capital Region based on the Generalized Random-Tessellation Stratified (GRTS) method (Stevens and Olsen, 2004). The sample consists of a grid of 32 meter x 32 meter cells, each having a unique ranking number. This so-called master sample is used as a basis to draw samples for different Natura 2000 habitat types in Flanders. A sample with sample size <em>n</em> for a certain habitat type is selected as follows: (1) select all grid cells of the master sample that overlap with the sampling frame of the target habitat type and (2) select the <em>n</em> grid cells with the lowest ranking number.</p>
Monitoring of Microtus ochrogaster and Microtus pennsylvanicus populations in three different habitats in east-central Illinois, 1972 to 1997.
Populations of 2 species of arvicoline rodents, the prairie vole (Microtus ochrogaster) and meadow vole (Microtus pennsylvanicus), were monitored monthly from 1972-1997 in three distinct habitats: restored tallgrass prairie, bluegrass (Poa pratensis) and alfalfa (Medicago sativa). The study sites were located in the University of Illinois Biological Research Area (Phillips Tract) and Trelease Prairie. Tallgrass prairie was the original habitat of both species in Illinois. Bluegrass, an introduced species, represents the more common habitat in which the two species can be found today in Illinois. Alfalfa, an atypical habitat, provides an abundant source of high-quality food for both species. At each station, one wooden multiple-capture live-trap was placed. Every month, a two-day period of prebaiting was followed by a 3-day trapping session. The data include the species, individual identification, grid station, sex, reproductive status and body mass. Over the span of 25 years, three trapping sessions monthly were conducted to cover the three habitats, dedicating three weeks each month. Several papers have been based on these data.
Salmonid habitat use monitoring used to determine effectiveness of habitat improvement projects in the Sacramento River, CA
Overview The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains five datasets. Enclosure Study – Growth Data This dataset covers enclosure studies that examined salmonid growth rates in the Sacramento River and focused on assessing effectiveness of salmonid habitat improvement projects. Data was collected in July and August 2019 from project sites, constructed habitat project sites, and control sites where no treatment is planned. Six enclosures with juvenile Fall Run Chinook salmon from Coleman National Fish Hatchery were placed in each habitat type. Fish growth was tracked for approximately 6.5 weeks. Annual reports summarize the survey findings. Enclosure Study – Gut Contents Data This dataset covers enclosure studies that examined salmonid growth rates in the Sacramento River and focused on assessing effectiveness of salmonid habitat improvement projects. Data was collected in July and August 2019 from project sites, constructed habitat project sites, and control sites where no treatment is planned. Six enclosures with juvenile Fall Run Chinook salmon from Coleman National Fish Hatchery were placed in each habitat type. Enclosures remained in the river for approximately 6.5 weeks. At the end of the study, fish were euthanized, and we dissected their guts and enumerated the taxa found. Annual reports summarize the survey findings. Microhabitat Use Data This dataset covers salmonid microhabitat use conducted in the Sacramento River and focused on assessing effectiveness of salmonid habitat improvement projects. Surveys are conducted roughly monthly and include pre-project sites, constructed habitat project sites, and control sites where no treatment is planned. Based upon habitat inventory data, annually identify which habitat units within each side channel will be selected for the collectio
Hubbard Brook Wildlife Monitoring Project: Assessing wildlife population presence, activity and habitat use through continual camera trap monitoring, 2018
Monitoring of wildlife at Hubbard Brook is essential to understand how these species are responding to forest and environmental condition over time, while also placing those wildlife species in the context of ecosystem structural and functional attributes. The presence and persistence of wildlife species common to an area can indicate suitable habitat conditions as well as refugia for less common species. Changes in species presence and activity, such as fewer to no sightings, may point to shifting conditions not suitable to the species missing from the area. Camera trap monitoring allows for continuous, non-obtrusive observation of many different species of wildlife and can be used as part of our understanding of current suitability of habitat condition. To better understand integrated forest condition, we established a camera trap network located at the Hubbard Brook Experimental Forest in the White Mountains of central New Hampshire. The cameras have logged over 1,500 wildlife observations, confirming the presence of many species, including those not previously reported (pine marten and river otter). A total of 15 mammal species have been detected and have also been effective at detecting some bird species, including the Northern Harrier. Natural history observations have provided insight into the lives of the species detected, including reproduction (Bull moose following cow during rut, moose calves, deer fawns), predation (red fox with snow-shoe hare) and presence of parasites (winter ticks on moose with hairless shoulders). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files
<p>The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository.</p> <p>We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses.</p> <p>The index scores of each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes_average.csv</em></p> <p>The habitat specific index scores for each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes.csv. </em></p> <p>Column name descriptions are available in the text file: <em>Column_name_descriptions_20220301</em></p> <p>The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script <em>01_Workflow sources</em> the first 9 scripts in the <em>scripts</em> folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript.</p> <p>A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.</p>
Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA
<p>Nucleic acids released by organisms and isolated from environmental substrates are increasingly being used for molecular biomonitoring. While environmental DNA (eDNA) has received attention recently, the potential of environmental RNA as a biomonitoring tool remains less explored. Several recent studies using paired DNA and RNA metabarcoding of bulk samples suggest that RNA might better reflect "metabolically active" parts of the community. However, such studies mainly capture organismal eDNA and eRNA. For larger eukaryotes, isolation of extra-organismal RNA will be important, but viability needs to be examined in a field-based setting. In this study we evaluate (a) whether extra-organismal eRNA release from macroeukaryotes can be detected given its supposedly rapid degradation, and (b) if the same field collection methods for eDNA can be applied to eRNA. We collected eDNA and eRNA from water in lakes where fish community composition is well documented, enabling a comparison between the two nucleic acids in two different seasons with monitoring using conventional methods. We found that eRNA is released from macroeukaryotes and can be filtered from water and metabarcoded in a similar manner as eDNA to reliably provide species composition information. eRNA had a small but significantly greater true positive rate than eDNA, indicating that it correctly detects more species known to exist in the lakes. Given relatively small differences between the two molecules in describing fish community composition, we conclude that if eRNA provides significant advantages in terms of lability, it is a strong candidate to add to the suite of molecular monitoring tools.</p>
Fig. 1 in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat
Fig. 1. Site map for ABHab points at LPS: dashed line is approximate separation of terra firma and flood plain forest.
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)
Fig 5 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 5. Phylogenetic tree based on a portion of the COI barcoding segment showing the relationships of selected non-target moth specimens (g54xxx) isolated from fall armyworm pheromone traps relative to selected GenBank sequences. GenBank sequences are indicated by species name followed by accession number. Fall armyworm R-strain and fall armyworm C-strain are consensus sequences for the 2 fall armyworm host strains.
Fig 3 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 3. Field screening of home-made trap design (Jar2 and Jar4) in comparison to Unitrap model using pheromone lures (all combined) over 2 maize cropping systems (maize monoculture and maize-cowpea intercrops) during the second planting season. The traps were installed on 30 Sep 2019 during the second maize growing season, and the moth collection period covered Oct to Dec. The data denotes average numbers per trap type for overall 3-d intervals moth collections with standard errors.
Fig 2 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 2. Preliminary field test of pheromone traps using the 2-component fall armyworm pheromone PSU lure during the first maize growing season: comparison between home-made Jar2 trap and Unitrap model (A) (average number per trap type for overall weekly moth collections; error bars represent standard error and different lowercase letters denote statistical difference), and fluctuation in moth trap catch of the Unitrap-2-component lure combination (B) (moth collections were done every 3 d).
Fig 4 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 4. Moth trap catch of 3 pheromone lures over 2 cropping systems (maize monoculture and maize-cowpea intercrops) using Unitraps. The traps were installed on 30 Sep 2019 during the second maize growing season and allowed to collect moths Oct to Dec 2019. The 4-component lure type (4C) contained Z9-14:Ac (78.3%), (Z)-11-hexadecenyl acetate (Z11-16:Ac) (3.6%), Z7-12:Ac (11.2%), and (Z)-9-dodecenyl acetate (Z9-12:Ac) (7.0%); whereas the 3-component lure type (3C) was composed of Z9-14:Ac (66.1%), Z11-16:Ac (4.7%), and Z7-12:Ac (29.3%); and the 2-component lure type (2C) of Z9-14:Ac (90.5%) and Z7-12:Ac (9.5%). The data represents average numbers for overall 3-d intervals moth collections.
Fig 1 in Monitoring Spodoptera frugiperda in Benin: assessing the influence of trap type, pheromone blends, and habitat on pheromone trapping
Fig 1. Traps used in study: commercially available Unitrap (A); home-made Jar2 trap constructed from 2 L plastic jar (B). The Jar2 trap was designed by G.T. TepaYotto and J.K. Winsou.
Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat [Dataset]
<p>This repository contains data used in Jussila et al. 2023 paper "Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat" (submitted). The files uploaded in the repository include (i) spatial polygon data of flark mires located in Finnish aapa mire occurrence zone, (ii) polygon subset of the flark mires observed in the study, focused on mires belonging to Natura 2000 network (ii) monthly information for April-September period in 2017-2020 of climatic water balance and Sentinel-2 -derived wetness metrics, as average values per mire, and (iii) training point data used to train the decision tree model which was used in the study to detect wet flark surfaces in the studied aapa mires. Retrieving the metrics from Sentinel-2 satellite imagery for the analysis of variability in wetness was executed with Sentinelhub Batch statistical API, and the process is documented in project GitHub repository: <a href="https://github.com/sykefi/feo-aapa">https://github.com/sykefi/feo-aapa</a>. </p> <p>Additional information of data is provided in the README file.</p>
Robotic Monitoring of Alpine Screes: a Dataset from the EU Natura2000 habitat 8110 in the Italian Alps
<p>Data collected between the 19th and the 21rd of July 2022, in Valfurva, 23030 (SO), Italy, within the Stelvio National Park, located inside the Natura 2000 SPA IT2040044. The data acquisition has been conducted by a team composed of both robotic engineers and plant scientists. The platform used to collect the data is the ANYmal C quadrupedal robot.</p> <p> </p> <p>The dataset contains two different sets of data:</p> <p>1) typical and early warning species data - videos of seven different typical species of the habitat 8110 and one early warning species.</p> <p>2) monitoring mission data - video of the monitoring mission, robot status and point clouds, pictures and videos taken by the robot during the autonomous surveys.</p> <p> </p> <p>This dataset has a multidisciplinary scope and can be used by researchers in several fields. For instance, point clouds and information about the robot state could be used by robotic engineers to test or validate their own methods as well as benchmark the robot performance. On the other hand, plant videos and images recorded by the robot could be used by botanists to assess the quality of this information as well as the habitat's conditions, or by computer scientists interested in testing their AI algorithms for species detection and classification.</p>
Robotic Monitoring of Dunes: a dataset from the EU habitats 2110 and 2120 in Sardinia (Italy)
<p>Data collected between the 16th and the 19th of May 2022, in Platamona, 07037 (SS), Sardinia, Italy, within the Natura 2000 SAC ITB010003. The data acquisition has been conducted by a team composed of both robotic engineers and plant scientists. The platform used to collect the data is the ANYmal C quadrupedal robot. </p> <p>The dataset contains three different sets of data: <br> 1) species data - pictures and videos of three different typical species of the habitat 2110 and 2120 and one alien species.<br> 2) 3D mapping data - robot status and point cloud<br> 3) monitoring mission data - robot status and pictures and videos taken by the robot during the autonomous surveys.</p> <p>This dataset has a multidisciplinary scope and can be used by researchers in several fields. For instance, point clouds and information about the robot state could be used by robotic engineers to test or validate their own methods as well as benchmark the robot performance. On the other hand, plant videos and images recorded by the robot could be used by botanists to assess the quality of this information as well as the habitat's conditions, or by computer scientists interested in testing their AI algorithms for species detection and classification.</p>
Robotic Monitoring of Forests: a Dataset from the EU habitat 9210* in the Tuscan Apennines (Central Italy)
<p>Data collected between the 27th and the 28th of April 2022, in Chiusi Della Verna, Arezzo 52010 (AR), Italy, inside the Natura 2000 SAC IT5180101. The data has been acquired mainly by the legged robot ANYmal C guided by a team of both roboticists and plant scientists. </p><p>The dataset contains four different sets of data: </p><p>1) species data - photos of four indicator species of the habitat 9210 (3 typical species and 1 early warning species).</p><p>2) mapping data - three dimensional point clouds of the habitat environment.</p><p>3) autonomous monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the autonomous mission.</p><p>4) teleoperated monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the teleoperated mission.</p><p>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. On the one hand, robotic engineers could, for instance, benchmark the performance of the robots and test or validate their own methods using the point clouds and the information about the robot state. On the other hand, botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant videos and images that the robot captured, or computer scientists could test their AI algorithms for identifying and classifying different species using these data.</p>
Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA
Open the record for dataset details and reuse information.
Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances
<p class="StandardohneEinzug">Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat-based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post-disturbance salvage logging affect habitat suitability for two ungulates, roe deer (<i>Capreolus capreolus</i>) and red deer (<i>Cervus elaphus</i>), over 32 years in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow versus bark-beetle-affected stands). Salvage logging altered the suitability of bark beetle-affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long-lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 years. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multi-year telemetry data into single, multi-temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat-based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance</p>
Data from: environmental DNA reveals temporal variation in mesophotic reefs of the Humboldt upwelling ecosystems of central Chile: towards a baseline for biodiversity monitoring of unexplored marine habitats
<p>Temperate mesophotic reef ecosystems (TMREs) are among the least known marine habitats. Information on their diversity and ecology is geographically and temporally scarce, especially in highly productive large upwelling ecosystems. Lack of information remains an obstacle to understanding the importance of TMREs as habitats, biodiversity reservoirs and their connections with better-studied shallow reefs. Here, we use environmental DNA (eDNA) from water samples to characterize the community composition of TMREs on the central Chilean coast generating the first baseline for monitoring the biodiversity of these habitats. We analyzed samples from two depths (30 and 60m) over four seasons (spring, summer, autumn, and winter) and at two locations approximately 16 km apart. We used a panel of three metabarcodes, two that target all eukaryotes (18S rRNA and mitochondrial COI) and one specifically targeting fishes (16S rRNA). All panels combined encompassed eDNA assigned to 42 phyla, 90 classes, 237 orders, and 402 families. The highest family richness was found for the phyla Arthropoda, Bacillariophyta and Chordata. Overall, family richness was similar between depths but decreased during summer, a pattern consistent at both locations. Our results indicate that the structure (composition) of the mesophotic communities varied predominantly with seasons. We analyzed further the better-resolved fish assemblage and compared eDNA with other visual methods at the same locations and depths. We recovered eDNA from nineteen genera of fish, six of these have also been observed on towed underwater videos, while thirteen were unique to eDNA. We discuss the potential drivers of seasonal differences in community composition and richness. Our results suggest that eDNA can provide valuable insights for monitoring TMRE communities but highlight the necessity of completing reference DNA databases available for this region.</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.