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799 results for “abundance data”
Long-term Mollusc Population Abundance and Size Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program
This data set includes long-term observational data on mollusc species abundance and size distribution at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites annually in October. Molluscs were also collected from an additional high marsh Juncus zone (n = 4 quadrats) at several sites beginning in 2009. The molluscs were returned to the lab, preserved in ethanol, identified and counted to determine species abundance and density in each plot. The length of each measurable individual was then determined using calipers or an ocular micrometer mounted in a stereomicroscope to determine mollusc size. Population abundance and size measurement data are reported separately by site, zone, plot and species because analyses were performed at different times, specimens were not individually identifiable, and not all individuals were measureable. This data set includes cumulative long-term observations from 2000 to 2022, and will be updated annually to include the prior year observations.
Long-term Burrowing Crab Population Abundance Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program
This data set includes long-term observational data on burrowing crab abundance at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Crab abundance was determined by performing surveys of crab hole occurance within replicate 625 square centimeter quadrats and converting the counts to number per square meter. Surveys were performed annually during October within the mid-marsh and creek bank zones at GCE marsh study sites 1 through 10 (i.e. n = 4 per zone at each site). Surveys were also performed in an additional high marsh Juncus zone at several sites beginning in 2009 (i.e. n = 4 quadrats per site). Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum, Sesarma reticulatum and Panopeus spp. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts. This data set includes cumulative observations from 2000 to 2023, and will be updated annually to include the prior year observations.
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Data and Code in support of Caterpillar abundance in a northern hardwood forest: exogenous effects, endogenous feedbacks, and multidecadal trends.
In this study, we analyzed caterpillar abundance and biomass measured over 50 years (1970 - 2021) in the Hubbard Brook Experimental Forest, New Hampshire, USA. We tested mechanisms for determination of caterpillar abundance that included weather, host plant quality, and predator abundance. This dataset includes data, R code, and spatial files supporting this study. 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.
Diatom Species Abundance Data from LTER Caribbean Karstic Region (CKR) study (FCE) in Yucatan, Belize and Jamaica during 2006, 2007, 2008
Several studies have shown that within the Florida Coastal Everglades, periphyton mat properties, (incuding biomass, nutrient and organic content, and community composition) vary predictably in response to water quality.The Florida Coastal Everglades (FCE) wetland system is very similar with respect to climate, geology, hydrology and vegetation, to wetlands found in Jamaica, the Yucatan region of Mexico and parts of Belize. This study was therefore conducted to ascertain (i) the level of similarity between the periphyton diatom communities from karstic wetland sites in Belize, Mexico, Jamaica and comparable sites within the FCE, (ii) the relationship between periphyton biomass, TP levels and diatom community composition at these sites, and (iii) the feasibility of employing diatoms as indicators of water quality at these sites, using models relating diatom community composition to water quality from comparable sites within the FCE. Multiple wetland sites in Jamaica, the Yucatan region of Mexico and parts of Belize were visited between 2006 and 2008, during wet and dry seasons. At each site physico-chemical data were collected along with periphyton samples. The periphyton samples were processed in accordance with standard methods to obtain biomass, organic content and TP measures, and to identify and enumerate diatom and soft algae species. Various aspects of the diatom communities were then compared to previously compiled data on diatom communities from various parts of the FCE. SIMI analysis was used to determine the level of similarity between the systems and Non-Metric Multidimensional Scaling was used to identify relationships between diatom communities and water quality.
Relative Abundance Diatom Data from Periphyton Samples Collected from the Greater Everglades, Florida USA from September 2005 to November 2014
This data package contains relative diatom taxon abundances collected annually during the wet season between 2005 and 2014 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units and each year, random coordinates are 'drawn' within each PSU and one sampleable draw is visited in each. Sampled periphyton is processed for diatoms, slides are prepared, and 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental, periphyton biomass, and soft algal abundance datasets. Post-2014 data are available upon request to the project PI, Evelyn Gaiser.
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012
This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Data Associated with Chemical Cartography with APOGEE: Two-process Parameters and Residual Abundances for 288,789 Stars from Data Release 17
<p>Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity log(g) in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star's measurements into the amplitudes A_cc and A_Ia and the element-by-element residuals from this two-parameter fit. Here we present the log(g)-calibrated abundances, fit parameters, process amplitudes, and element-by-element abundance residuals of 288,789 stars (310,427 spectra) in APOGEE DR17 that accompany <a href="https://arxiv.org/abs/2403.08067" target="_blank" rel="noopener">the paper</a>.</p> <p>calibration_values_final.dat contains all derived calibration offsets, including the grids of log(g) calibration offsets and zero-point offsets for two-process model analysis. The first five rows of this catalog are reproduced in Table 2 of the paper.</p> <p>logg_calib_example.ipynb is a Jupyter notebook containing Python code to load calibration_values_final.dat, extract the log(g) calibration offsets for specific element, and apply calibration offsets to 10 sample stars.</p> <p>2process_residual_abund_catalog_final.fits is the catalog of 310,427 APOGEE DR17 spectra (288,789 unique stars) containing calibrated abundances, two-process fit parameters, and abundance residuals. A full listing of columns in this catalog is given in Table 5 of the paper.</p> <p>catalog_examples.ipynb is a Jupyter notebook containing Python code to load 2process_residual_abund_catalog_final.fits, cross match with other catalogs (using AstroNN and the APOGEE DR17 Globular Cluster Value-Added Catalog as examples), and make some example plots utilizing the cross-matched data.</p>
Meiofauna higher taxa abundance data from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide abundance data for meiofauna taxa determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.</p> <p>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. The organisms were extracted by decantation over a 32-μm sieve. The total number of individuals per taxon was counted and is presented as individuals per 10 cm<sup>2</sup>.</p> <p>In the framework of our monitoring, samples were primarily taken for a large-scale metabarcoding study on meiofauna communities. One core per station and sampling date was reserved for morphology-based community analyses. Here we present the results for the stations AH01, AH03, AH05, and PAP. We selected these stations because of their location at both ends and in the center of the impacted zone (AH01, AH03, AH05) and at the control site (PAP). The meiofauna (32–1000 µm) was mostly represented by Copepoda, Nematoda, Platyhelminthes, Gastrotricha, and some Annelida. We counted 27445 individuals in total, encompassing 10 higher taxa. We counted copepod nauplii separately due to their small body size. We defined the combined group “Plathyhelminthes+<em>Diurodrilus</em> sp.” because members of the annelid genus <em>Diurodrilus</em> sp. are not distinguishable from Platyhelminthes under the stereomicroscope.</p> <p>Here we present a Table on meiofauna higher taxa counts per 10cm<sup>2</sup> (as xlsx and tab-delimited file; including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The meiofauna abundance data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and metabarcoding analyses (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
Cyanobacteria abundance, cyanotoxin concentration, and water quality data for the upper San Francisco Estuary, California, USA: 2014-2019
The goal of these measurements was to quantify Microcystis abundance and microcystin concentration and associated water quality conditions during summer blooms in the upper San Francisco Estuary in California, USA. Blooms of harmful algae are a major ecological concern in the area because harmful algae produce toxins and other metabolites, which deteriorate water quality and negatively impact the aquatic ecosystem. Our research team collected biological, physical, and chemical data at 2-week to 4-week intervals during the summer and fall from 2014 through 2019. Data included surface measurements of Microcystis volume (area-based diameter) by microscopy (flowCAM digital imaging flow cytometry) and subsurface (1 m depth) measurements of Microcystis, Aphanizomenon and Dolichospermum cell abundance measured by quantitative PCR, cyanotoxin concentration (total microcystins, anatoxin a and saxitoxin) measured by protein phosphatase inhibition assay or enzyme linked immunosorbent assay, and a suite of water quality parameters (water temperature, dissolved oxygen, nutrient concentration, water transparency, specific conductance, turbidity, pH, and chlorophyll a concentration). Details for the field sampling and analytical methods are available in Lehman et al. (2017). We also performed shotgun metagenomic analyses to investigate biodiversity of cyanobacteria and other aquatic microorganisms and all the DNA sequencing data are publicity available (www.ncbi.nlm.nih. gov/; BioProject ID: PRJNA434758, Kurobe et al. 2018, Lehman et al. 2021). During the study, we experienced critically dry (2014 and 2015), below normal (2016 and 2018), and wet years (2017 and 2019), therefore data obtained in this study provided a unique opportunity to assess impacts of extreme conditions on the aquatic ecosystem (Kurobe et al. 2018, Lehman et al. 2020).
Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing
This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).
Rodent abundance and biomass data across grassland-shrubland ecotones at 3 sites in the Jornada Basin, 2004-ongoing
This is an example dataset for testing `jerald` and the Jornada IM system using dataset 210262010. Data here come from R’s `mtcars` example dataset. You can replace this abstract with one for your dataset.
Terrestrial gastropods abundance data along an elevational gradient within the Sonadora River watershed
The data set includes 3 files that contain abundance data for terrestrial gastropods along an elevational gradient within the Sonadora River watershed. Two files (1 and 2) contain data from the same transect but differ in the year during which they were collected (2007 and 2008). The third file (3) contains data from a separate elevational transect (sites were located at the same elevation as in files 1 and 2) in palm dominated forest within the same watershed that was collected during the same time period in 2008 as data from file 2. Note: Plots at 250 m of elevation were not sampled in 2008 on either transect and a plot at elevation 750 m in the palm transect was never sampled. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Aquatic macrophyte, snail, and crayfish abundance and richness data for ten lakes in Vilas County, WI, USA, 1987-2020
Data accompanying the paper Szydlowski et al. "Macrophyte and snail community responses to 30 years of population declines of invasive rusty crayfish (Faxonius rusticus)." Macrophytes and snails were sampled in ten lakes in Vilas County, Wisconsin, USA during summer sampling events in 1987, 2002, 2011, and 2020. Lakes had varying levels of invasion by F. rusticus, which affected measures of macrophytes and snails. Macrophytes were sampled using a point-intercept transect method and snails were sampled using different sampler types which were dependent on substrate. Macrophytes were sampled at 6-14 sites per lake and snails were sampled at 16-31 sites per lake. Crayfish were regularly sampled at either 24 or 36 sites per lake between 1987 and 2020. Overall, this dataset provides abundance and richness data for over 25 species of snails and over 40 species of macrophytes in 10 north temperate lakes.
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Data for: Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass
<p>This repository contains data for the paper: Bijleveld, A. I. et al. (2018) Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass. Scientific Reports 8, 3039, doi:10.1038/s41598-018-21285-1.</p>
Aggregated data of abundance indices
<p>For the reconstruction of flight peaks, an abundance index was calculated by relating records to search efforts based on the evidence of field activities left in the database by proficient observers. For this search-effort correction, we selected observers with at least 50 records of at least 10 butterfly species each year. As a measure of their collective search effort, we calculated the sum for each day of all the 100 × 100 m grid cells from which an observer had reported records (of any species, including non-butterflies; also including absence records). This method of ‘proven day-grid-visits’ has become the standard proxy for search effort when analyzing incidental observations of the portal waarnemingen.be. Day-grid-visits do not cover search effort completely, because records are not submitted from every visited hectare grid cell, but strongly correlates with it.</p> <p>We provide the raw data containing day (2009-2020), the X and Y coordinate of the centroid of the 100x100 m grid cell (In Lambert 72, EPSG:<em>31370</em> Projected coordinate system for Belgium), the number of peacock butterflies reported, and the number of hectare day grid visits. </p>
Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India
<p>This dataset contains data from the following publication:</p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 94: 748-757.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a><br> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a></p> <p><em>Corrigendum:</em></p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 97: 612-613.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a></p> <p><strong>Description of dataset:</strong></p> <p>The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below.</p> <p>Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded.</p> <p><strong>AUTHOR #1</strong></p> <p>1. Name: Hari Sridhar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria<br> 4. Email address: harisridhar1982@gmail.com<br> 5. ORCID: https://orcid.org/0000-0003-3286-0120</p> <p><strong>AUTHOR #2</strong></p> <p>1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>AUTHOR #3</strong></p> <p>1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p><br> <strong>Keywords:</strong> tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills</p> <p><br> <strong>Geographic Coverage:</strong></p> <p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p> <p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><br> <strong>Temporal Coverage:</strong></p> <p>1. Begins: 2005-09-01 (Year, Month, Day)</p> <p>2. Ends: 2006-10-31 (Year, Month, Day)</p> <p> </p> <p><strong>Dataset files:</strong></p> <p>Besides the <strong>00_README.txt</strong> file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below:</p> <p><strong>01_Transect_locations.csv</strong> — contains transect location details and descriptions</p> <p><strong>02_Transects_and_opportunistic_surveys.csv</strong> — contains main dataset of observations on line transect and opportunistic surveys</p> <p><strong>03_Opportunistic_observations_locations.csv</strong> — contains location details of opportunistic surveys</p> <p><strong>04_Lion-tailed_macaque_counts.csv</strong> — contains counts of lion-tailed macaque (<em>Macaca silenus</em>) troops</p> <p><strong>05_allmammals_raw.xls</strong> — raw data NOT for use, for reference only in original Microsoft Excel format</p> <p> </p> <p><strong>Data variables and descriptions:</strong></p> <p><strong>01_Transect_locations.csv</strong><br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> TransectLength_m: Length of line transect in metres<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum<br> StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum<br> EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum<br> EndLon: transect ending longitude in decimal degrees (E), WGS84 datum<br> MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum<br> MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum<br> ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum<br> RouteDescription: description of transect route</p> <p><br> <strong>02_Transects_and_opportunistic_surveys.csv</strong><br> eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number<br> occurrenceID: unique ID assigned to each occurrence (detection) along line transect<br> Sno: serial number<br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> locality: name of transect<br> Transectno: unique number assigned to each transect survey or resurvey<br> Replicate: number indicating repeat survey of same transect<br> SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment<br> Date: date of transect survey or opportunistic observation<br> Weather: Weather at time of survey<br> Habitat: Habitat where observation was made<br> Time: time in 24 h HH:MM format<br> verbatimIdentification: Identification as originally entered<br> scientificName: Scientific name of species or taxon observed<br> vernacularName: Common English name of species or taxon observed<br> Perpdist: Perpendicular distance in metres<br> Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month)<br> individualCount: number of individuals counted, taken as minimum 1 if not noted in field<br> rawNumber: number as originally entered<br> DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track<br> verbatimDetection: raw entry corresponding to previous column<br> Height: height of observed animal above ground in metres<br> occurrenceRemarks: remarks on occurrence</p> <p><br> <strong>03_Opportunistic_observations_locations.csv</strong><br> locality: name of place or transect where opportunistic observation was made<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres)</p> <p><br> <strong>04_Lion-tailed_macaque_counts.csv</strong><br> Sno: Serial number of entry<br> Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted<br> Date: Date of observation<br> Time: Time of observation in 24h HH:MM format<br> Weather: Weather<br> Groupid: ID of Lion-tailed macaque troop, if known<br> Total: Total number of individuals recorded<br> AM: number of adult males<br> AF: number of adult females<br> A: number of adults (unsexed)<br> SA: number of sub-adults (unsexed)<br> SAM: number of sub-adult males<br> SAF: number of sub-adult females<br> JUV: number of juveniles<br> INF: number of infants<br> CARINF: number of infants carried by mother<br> UNID: number of unclassified<br> Remarks: other notes</p> <p> </p> <p><strong>05_allmammals_raw.xls</strong></p> <p>Raw data file in Microsoft Excel format -- for reference only (not advised for use)</p> <p><br> <strong>ADDITIONAL NOTES</strong><br> General notes taken about survey:<br> Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor.<br> 36TH hpb transect - very poor visibility on one side as it is very steep<br> Giant squirrels present within LTM troops might go undetected. Need to look carefully and check every movement<br> KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres<br> SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants<br> BAN - Ignore detections after 2.05 KM for first two replicates<br> KSPV 26/01/2006 - 1 GS not detected on transect detected while returning at < 40 m<br> Anaigundi - Transect in december strayed slightly from correct path<br> Var 30/01/2006 transect Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case<br> should I consider only january and afterwards for MGH numbers since vocal activity is much higher then?<br> TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting<br> Do NL individuals move solitarily; what average group size to use<br> visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA<br> rained on 1st & 2nd of March after a long dry spell<br> KSPV 31/03/06 - Could have missed some calls because of cicada noise<br> KSWT 01/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Great hornbills seem to be more vocal during april. To do with end of nesting??<br> malabar grey hornbills more vocal from february onwards</p>
BTO Garden BirdWatch: Weekly butterfly abundance data for modelling trends in UK gardens
<p>Dataset used to estimate annual abundance indices and trends for UK butterflies in gardens, covering the period 2007 to 2020.</p> <p>Data have been collected as part of the British Trust for Ornithology (BTO) Garden BirdWatch (GBW) survey. GBW is a structured, citizen science monitoring programme whereby volunteers record weekly abundances of various bird, invertebrate, mammal, reptile and amphibian species in (predominantly suburban and rural) gardens. See <a href="http://www.bto.org/gbw">www.bto.org/gbw</a> for further information about the survey. </p> <p>This dataset has been pre-filtered to meet criteria for inclusion in the modelling of butterfly species trends, as described by <a href="https://doi.org/10.1111/icad.12645">Plummer et al 2023</a>. Please refer to the 'readme' file for further details.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</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.