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322 results for “census”
Linked collectors and determiners for: Census of plant species in Bonou forest reserve (Southern Benin).
Natural history specimen data linked to collectors and determiners held within, "Census of plant species in Bonou forest reserve (Southern Benin)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1d37d177-141a-4bcd-afdb-d5c80d326504">https://bionomia.net/dataset/1d37d177-141a-4bcd-afdb-d5c80d326504</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1d37d177-141a-4bcd-afdb-d5c80d326504">https://gbif.org/dataset/1d37d177-141a-4bcd-afdb-d5c80d326504</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Registros biológicos recogidos en el marco del proyecto Census of Marine Life Componente Natural Geography in Shore Areas - NAGISA Santa Marta.
Natural history specimen data linked to collectors and determiners held within, "Registros biológicos recogidos en el marco del proyecto Census of Marine Life Componente Natural Geography in Shore Areas - NAGISA Santa Marta". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1aa3d0ff-020b-4456-985f-cf88b4e0fb23">https://bionomia.net/dataset/1aa3d0ff-020b-4456-985f-cf88b4e0fb23</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1aa3d0ff-020b-4456-985f-cf88b4e0fb23">https://gbif.org/dataset/1aa3d0ff-020b-4456-985f-cf88b4e0fb23</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Census of the flora of Benin.
Natural history specimen data linked to collectors and determiners held within, "Census of the flora of Benin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0eac2479-e5d8-4e9d-bc7d-9dc689c159d5">https://bionomia.net/dataset/0eac2479-e5d8-4e9d-bc7d-9dc689c159d5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0eac2479-e5d8-4e9d-bc7d-9dc689c159d5">https://gbif.org/dataset/0eac2479-e5d8-4e9d-bc7d-9dc689c159d5</a>. Formatted as a Frictionless Data package.
The 2021 PyAOS Census
<p>The Python for Atmosphere and Ocean Science (PyAOS) <a href="http://https://pyaos.github.io/">website</a> and mailing list was established almost a decade ago to provide information and resources to the user community. In order to keep the site up-to-date, the first ever PyAOS census collected responses from May to July 2021. There were 144 participants in the survey and the results are summarised at:</p> <p>https://pyaos.github.io/census/</p> <p>This repository includes:</p> <ul> <li>README.txt: A copy of the survey questions</li> <li>census.csv: Responses to all single-answer questions</li> <li>question*.csv: Responses for questions where more than one answer could be given.</li> <li>*.py: Python scripts that were used to help clean the responses to question 16</li> </ul>
Crosswalk between ISRM grid and 2010 census block
<p>This dataset contain two files: (1) a crosswalk between <a href="https://zenodo.org/record/2589760">InMAP Source-Receptor Matrix (ISRM)</a> grid and census block (based on 2010 census); and (2) a shapefile of ISRM grid. </p>
Great Britain coastline boundary (modified from 2011 Census boundary data) [GeoJSON]
<p><strong>Original purpose</strong></p> <p>This coastline boundary dataset was originally derived for research on population proximity to the UK coast. It required adaptation of boundary files in order to prevent areas close to major rivers from being counted as ‘coastal’. With no single definition of what ‘coastal’ is, we took a decision to cut off the coastline where major estuaries/rivers narrowed to approximately 1km. The original publication that used this approach and informed the development of the dataset is cited below (Wheeler et al, 2012).</p> <p>Please note therefore that this is a somewhat arbitrary definition of what is coastal, and you will need to make sure this definition is appropriate for your application for this to be useful.</p> <p><strong>Method & Data Format</strong></p> <ul> <li>Original source data: UK Census 2011 Lower-layer Super Output Areas / Data Zones – full resolution / Mean High Water version.</li> <li>LSOA/DZ boundaries were dissolved to create outline boundary at Mean High Water.</li> <li>Major estuaries/rivers were manually truncated where they narrowed to approximately 1km width.</li> <li>Data are provided as a GeoJSON file</li> <li>Co-ordinate system is British National Grid (EPSG 27700)</li> </ul> <p><strong>Original data source & copyright</strong></p> <p>This boundary dataset was derived from Ordnance Survey/Office for National Statistics/Scottish Government data, under Open Government Licence. Its use/re-use is dependent on appropriate citation and acknowledgement of the original source data.</p> <p>Licence: Adapted and redistributed under Open Government Licence: <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/</a></p> <p>Copyright statements to appear on any maps/publications containing these data:</p> <p><strong>Contains National Statistics data © Crown copyright and database right 2012</strong></p> <p><strong>Contains Ordnance Survey data © Crown copyright and database right 2012</strong></p> <p><strong>Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (2012).</strong></p> <p> </p> <p><strong>Citation and Attribution</strong></p> <p>The original source of the approach and methodology for this coastal definition should be cited as:</p> <p>Wheeler, B.W., White, M., Stahl-Timmins, W., Depledge, M.H., 2012. Does living by the coast improve health and wellbeing? Health and Place 18: 5, 1198-1201. doi: 10.1016/j.healthplace.2012.06.015</p> <p>The boundary dataset requires the copyright statements as above to be stated on any publication/redistribution.</p> <p>The adapted data are redistributed here under CC-BY Licence - <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Trait records of marked Salvia nemorosa L. individuals censused annually between 2021-2023 in 13 localities of the Great Hungarian Plain
Open the record for dataset details and reuse information.
Liana species of the Barro Colorado Island 50-ha plot 2007 and 2017 censuses
Open the record for dataset details and reuse information.
A new Double Observer based census framework to improve abundance estimations in mountain ungulates and other gregarious species with a reduced effort
Open the record for dataset details and reuse information.
Sagehen Creek Creel Census, 1953-1961
Data package contains two data tables documenting surveys of angler catch in the Sagehen Creek. The Creel Census was conducted during the summer months of 1953-1961 by a range of surveyors. An additional data table containing information about trout planting in Sagehen Creek prior to the Census is also included.
Long Term Research in Environmental Biology: Demographic census data for thirty natural populations of American Ginseng: 1998-2016
In 1998, formal demographic censusing of wild ginseng (Panax quinquefolius L.) populations was initiated in West Virginia. By 2004, thirty populations had been added to the census effort, spanning seven states (IN-2, KY-6, MD-1, NY-2, PA-2, VA-5, WV-12) and a wide variety of land use histories and eastern deciduous forest communities. The censusing effort continued without interruption at all populations until June, 2016. Annually, each population was visited twice. The first visit generally occurred between late May and the end of June. The second visit generally occurred in the first three weeks of August. The purpose of the spring census was to assess the population status at the time of year when the largest number of individuals were visible aboveground (post-germination, prior to substantial losses due to browsing and other causes). Detailed measures of plant size were made, with an emphasis on total leaf area calculation. In addition, a variety of plant condition notations were made, with the ultimate goal of determining mortality and recruitment in the population, as well as individual size transitions. The primary purpose of the second census each year was to assess seed production on each plant. In addition, further notations of plant condition were made to assess changes over the growing season. To maintain methodological consistency with field personnel turnover, the lead author participated in fieldwork throughout the study, visiting each population at least once every two years. In addition, after being trained themselves, graduate students trained undergraduate conservation interns to assure consistent methods were used each year. The data are suitable for demographic modeling, and the unique spatial and temporal extent allow the exploration of important questions about variability in population growth and viability of ginseng, America’s premiere wild harvested medicinal plant.
Tree census, demography, and exposed canopy area data at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC from 1993 to 2016
The five Terrestrial Gradient sites were established in the early 1990s as part of the 1990 Coweeta LTER Renewal. The original terrestrial gradient sites were 20 x 40-m. In the late 1990s the plots were expanded to 80 x 80-m and later (around 1998) they were slope-corrected by Clark's lab using survey equipment. Much of the Coweeta LTER “core” datasets have been collected from the gradient plots. This study is one of the long-term studies that are ongoing with defined sampling intervals. The tree demography and census study consists of trees census every two years and seeds collected ~5 x each year.
Bird observations collected by volunteers along census tracks in the Parker River National Wildlife Refuge, intervals of observations are approximately twice a month.
This file contains bird observations collected by volunteers along census tracks in the Parker River National Wildlife Refuge, Massachusetts. Intervals of observations are approximately twice a month.
Nesting seabird census of Hog Island and Cobb Island of the Virginia Coast Reserve 1991
Nesting seabird census of Hog Island and Cobb Island of the Virginia Coast Reserve 1991. Includes a count of birds or nests observed by species.
Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR
<p>Lightcurves and spectra presented in the paper "Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR", Karambelkar et al. 2021.</p>
Stem data from first three censuses on the University of California Santa Cruz Forest Ecology Research Plot
<p>Here we present stem-based data from the first three censuses of the 16-ha UC Santa Cruz Forest Ecology Research Plot, located in the Mediterranean climate forest of coastal central California, USA. The forest includes both mixed evergreen forest and redwood-dominated forest, and is recovering from significant logging disturbance in the early 20th century. Each woody stem with diameter larger than 1 cm has been mapped, tagged, identified, and measured, with censuses on a ~5-year interval. The first census (FERP1) began in 2007, and included just 6 ha. During FERP2, begun in 2011, the plot was expanded to 16 ha. The FERP3 census was begun in 2017. The community includes 34 woody species, including 4 gymnosperm and 9 angiosperm tree species, 18 species of shrubs, and 3 species of lianas. Across the three censuses, we report identity, size, location, and mortality data from 48,556 stems of 31,206 mapped individuals.</p>
Tropical forest seedling census data from Danum Valley, Sabah, Malaysia (2019-2021)
<p><strong>Data Link</strong></p> <p>This is a link to a dataset affiliated with SEARRP but that is stored in a different repository. Please find the link to the dataset below, and be sure to cite the data correctly via that repository.</p> <p><strong>Correct Citation</strong></p> <p>Burslem, D.F.R.P.; Banin, L.F.; Bartholomew, D.C.; Bin Suis, M.A.F.; Bittencourt, P.R.L.; Chapman, D.; Dent, D.H.; Hayward, R.M.; O’Brien, M.J.; Rowland, L.M. (2022). Tropical forest seedling census data from Danum Valley, Sabah, Malaysia, 2019-2021. NERC EDS Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/c1813d0d-193f-4f23-82c6-333d5d099b42</p> <p><strong>Abstract</strong></p> <p>Southeast Asian tropical forests have been subjected to recent intense pressure due to selective logging and widespread clearance for Oil Palm cultivation. Consequently there is an emerging interest in restoring degraded forests using either natural regeneration or active restoration treatments. However, the reproductive biology of Southeast Asian tropical forest trees limits research on the effectiveness of these approaches, because most large canopy trees only flower and fruit very rarely. These sporadic mass reproductive events are responsible for establishing new cohorts of seedlings that grow up to become the next generation of adult canopy trees, and it is critical to discover whether the success of these episodic attempts at regeneration is as great in forests that have been degraded by logging as they are in primary forests, and whether the processes leading to seedling recruitment are restored effectively in forests where treatments such as tree planting and climber cutting have been applied. However, because these regeneration events occur so infrequently and unpredictably it is very difficult to incorporate them into the conventional planning cycle for research, despite the critical importance of the events that occur early in the life cycle of trees to future forests. In this project we will rapidly establish sampling sites in Sabah, Malaysia, where we know that a mass flowering of canopy trees was initiated in May 2019, for the first time since 2010. We aim to compare the amount and diversity of fruits and seedlings produced during this masting event in primary (undisturbed, unlogged) forests, and in adjacent forests that have been logged and either left to regenerate naturally or restored by planting tree seedlings and maintaining them for five years by climber cutting. Because the restoration of logged forests began more than 20 years ago, the original cohort of planted seedlings are now, in some cases, large canopy trees that may contribute seeds and seedlings for the first time during the reproductive event this year. We will also measure the expression of traits that determine how plants capture and use resources such as light and nutrients for the most common species that occur in each of the three types of forest, which will determine whether the community of seedlings that establish in the restored forests functions in a more similar way to that in the undisturbed primary forest than in the forests left to regenerate naturally after logging. A key focus on this study will be on species of the dominant family of canopy and emergent trees, the Dipterocarpaceae, which are targeted for logging. Logged forests possess a lower density of large reproductively mature dipterocarp individuals, and a key aim of restoration is to re-establish the dominance and diversity of this family by planting and maintaining dipterocarp seedlings. Dipterocarps possess an unusual trait for the tree flora of tropical forests, which is that they form mutualistic associations with root-colonising ectomycorrhizal fungi (ECM), whereas most other species in the forest form a different type of root association with arbuscular mycorrhizas (AM). Our recent research has shown that ECM seedlings benefit from proximity to a high density of ECM adults, possibly because they exchange resources through a common below-ground fungal network and because ECM species suppress root pathogens. In contrast, AM seedlings have lower survival when located close to a high density of adults of the same species. A final aim of our project is to test whether the beneficial effects of high adult density for ECM species is reduced in logged forests where the density of ECM adults is much lower, and whether these effects are offset by restoration. This research will therefore contribute results that are vital to understanding how Southeast Asian forests regenerate during masting events, and whether the negative effects of logging can be mitigated by restoration.</p> <p><strong>Link to project website:</strong> <a href="https://gtr.ukri.org/projects?ref=NE%2FT006560%2F1">GtR (ukri.org)</a></p> <p><strong>Link to data repository:</strong> <a href="https://doi.org/10.5285/c1813d0d-193f-4f23-82c6-333d5d099b42" target="_blank" rel="noopener">DOI</a></p>
Tree census data associated to 'Large-scale informative priors to better predict the local occurrence rate of a rare tree-related microhabitat'
<p>This repository contains two excel spreadsheets providing data about the trees studied in Cottais et al. study (doi : 10.1101/2024.11.28.625900):</p> <ul> <li>df_TreM.xlsx has 1462 lines (headers not included); each line corresponds to one oak tree sampled in 2021 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id : the id of the sampling plot where the tree is located</li> <li>Tree_Id : a unique id of the tree used in the BloBiForM project</li> <li>Species_Latin_Name : the latin name of the tree species ('Quercus_sp' for all trees given than only oaks are reported here)</li> <li>DBH_cm : the diameter at breast height of the tree in cm</li> <li>TreM : whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Life_Stage : whether the tree is a living tree or a snag</li> <li>Last_Logging_Year : the year of the last logging event in the stand where the tree is located</li> <li>conv : whether the last logging event occured less that 120 years before 2024 (conv=0; the stand is now classic high forest) or is more ancient (conv=1, the stand is being converted to high forest through sprout thinning)</li> </ul> </li> <li>DMH_2022.xlsx has 1190 lines (headers not included); each line corresponds to one tree sampled in 2022 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id: the id of the sampling plot where the tree is located</li> <li>Species_Latin_Name: the latin name of the tree species, obtained as a translation from french using the dictionnary in page 2; oaks are not the only species reported, but only oaks are used in Cottais et al. study;</li> <li>Species_French_Name : the french name of the tree species;</li> <li>DBH_cm: the diameter at breast height of the tree in cm</li> <li>TreM: whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Tree_Id: when the tree belongs to the cohort of the BloBiForM project, the tree id is reported; left empty otherwise</li> <li>Nb_brin: number of stems on the tree stump;</li> <li>Commentaire: any comments during fieldwork</li> </ul> </li> </ul>
Census and phenotype data supporting Drosophila adaptive tracking
<p>Direct observation of evolution in response to natural environmental change can resolve <span>fundamental questions about adaptation including its pace, temporal dynamics, and underlying</span> p<span>henotypic and genomic architecture. </span>W<span>e tracked evolution of fitness-associated phenotypes and</span> <span>allele frequencies genome-wide in ten replicate field populations of <i>Drosophila melanogaster</i></span><i> </i><span>over ten generations from summer to late fall. Adaptation </span><span>was evident over each sampling</span> <span>interval (1-4 generations) with exceptionally rapid phenotypic adaptation and large allele frequency shifts at many independent loci. The direction and basis of the adaptive response</span> <span>shifted repeatedly over time, consistent with the action of strong and rapidly fluctuating</span> <span>selection. Overall, we find clear phenotypic and genomic evidence of a</span><span>daptive tracking</span> <span>occurring contemporaneously with environmental change, demonstrating the temporally dynamic</span> <span>nature of adaptation.</span></p>
Too many to count: Using orthophotography to census an unharvested beaver (Castor canadensis) population in Ontario
<p>Various field methods exist to monitor wildlife populations and estimate trends in their distribution and abundance. For American beaver (Castor canadensis), aerial surveys provide a means to obtain abundance data over large areas and typically involve observers searching watercourses and shorelines for active beaver presence. Here, we describe a systematic aerial photo census we designed and executed in autumn to quantify annual beaver abundance on the 184 km2 Michipicoten Island, Ontario from 2015–2019. Aerial photos were stitched together into orthophoto mosaics after each census and visually searched for beaver food caches, with each food cache representing an independent beaver colony. Our methods revealed that beaver colony abundance declined substantially from a peak of 6.1 colonies/km2 in 2015, the highest reported across North America, to 0.4 colonies/km2 in 2018. Beaver abundance remained low through 2019. Though photographing the entire isolated study area required relatively little time and effort, even when beaver density was very high, post-census processing work was time-consuming. Lessons learned will improve the efficiencies of our future censuses and aid other researchers. Our method is advantageous over traditional aerial wildlife surveys in that it provides a digital and visual record that can be used for additional analyses. </p>
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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.