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83 results for “census data”
Massachusetts Historical Landcover and Census Data 1640-1999
An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).
Demographic census data for four perennial plants under experimental pollination treatments
These demographic data were collected to measure the effects of manipulated pollination treatments on the population dynamics of four iteroparous perennial plant species: Delphinium nuttallianum (Ranunculaceae), Hydrophyllum fendleri (Boraginaceae [Hydrophyllaceae]), Erigeron speciosus (Asteraceae), and Potentilla pulcherrima (Roseaceae). The pollination treatments consisted of Control corresponding to ambient pollination, Reduced for which 50% of open flowers on each individual were enclosed in mesh to exclude pollinators, Supplemented for which all receptive flowers were hand pollinated with outcross pollen, and Variable for which individuals received the Reduced treatment in ca. 50% of years. The full life cycle was characterized from at least four annual demographic censuses of tagged plants between 2017-2022, germination rates estimated in seed addition plots, and soil seed bank survival estimated from buried seed bags.
Tree Census Data of Tropical Dry Forest Succession in Permanent Plots at Palo Verde National Park, Costa Rica (1999–2004), Organization for Tropical Studies (OTS)
This data package contains tree census data from eight permanent forest plots established in 1999 across four successional sites within the tropical dry forest of Palo Verde National Park, Guanacaste, Costa Rica (10°21’N, 85°21’W). The plots were established to study forest structure, composition, and successional dynamics under different disturbance histories in the lowland dry forest ecosystem of northwestern Costa Rica. Each site represents a distinct successional stage, ranging from an early grass-dominated field (Jaragua) to an older partially disturbed remnant forest stand (Varillal). Two permanent 50×50 m plots were established at each site and subdivided into 10×10 m subplots. All woody stems with diameter at breast height (DBH) ≥ 10 cm were tagged, identified to species, and spatially referenced using X–Y coordinates within each plot. For multi-stemmed individuals, all stems meeting the diameter threshold were measured separately. Tree diameter, condition, and taxonomic identification were recorded during four measurement campaigns in 1999, 2001, 2002, and 2004. The dataset includes species identity, DBH, measurement year, individual condition, and subplot coordinates for each stem. These data provide a baseline for understanding forest regeneration, mortality, recruitment, and species composition changes in tropical dry forest succession under varying land-use histories. The dataset represents the historical component of an ongoing long-term monitoring program of forest succession conducted by the Organization for Tropical Studies at Palo Verde National Park, led, developed and supported by Eugenio González since its establishment in 1999.
Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
El Yunque Chronosequence Tree Census data
The El Yunque Chronosequence plots consist of four sites, El Verde 1 (EV1), Sabana 1 (SB1), Sabana 2 (SB2), and Sabana 3 (SB3), which are located at the edges of El Yunque National Forest at sites to the south of El Verde and Sabana Field Stations. The plots represent a range of successional stages representing areas in agriculture or recently abandoned in 1936 but reforested after 1950, and areas in agriculture or recently abandoned in 1977 and reforested since that time. They range in size from ~0.5 to 1 ha, vary in elevation from ~150m to 550m a.s.l. and span a wide range of ages and land use histories (Table 1). Plot Name Size Age Elevation EV1 10,000 m2 (1 ha) >62 yrs but < 76 yrs ~ 550m SB1 4,625 m2 (~0.5 ha) >62 yrs but not primary forest ~100-150m SB2 6,400 m2 (~0.6 ha) >35 yrs but < 62 yrs ~100-150m SB3 4,800 m2 (~0.5 ha) Primary forest ~100-150m One of these plots (EV1) is south of El Verde Field Station, on Forest Service land just over the property boundary.  This area was in agriculture in 1936 but appeared forested in a 1950 aerial photograph, and there are differences in forest structure and species composition consistent with the known differences in land use history. The other three Chronosequence sites are just south of the Sabana Field Station on Forest Service Land on the opposite side of the forest from El Verde. One plot (SB2) is located in young secondary forest in an area immediately adjacent to an old teak plantation forest. Another plot (SB1) is located in an area that was sparsely forested in 1936 and which appeared reforested in 1950. The third plot in Sabana (SB3) is located in a patch of primary “tabonuco†(named for the abundance of this tree species) forest on a steep slope on the west side of the Sabana River. 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 Foundati
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
Refined personal name data from the census book of Vodskaja pjatina
<p>The data contains approximately 36,000 personal names derived from medieval Russian documentation. More preciously, names are collected from an edited version of the census book of Vodskaja pjatina, which was one of the five administrative areas in the late 15<sup>th</sup> century Novgorod.</p> <p>Editions were compiled in parts and the first two, which cover the northernmost region, are called <em>Переписная окладная книга по новугороду вотской пятины</em> (1851, 1852)(POKV I‒II). The third part of the book series <em>Новгородские пистсовые книги</em> (1868)(NPK III) covers the southern and western parts of the study area.</p> <p>The process of obtaining the personal from the inscription has been following: First, editions of the census book were obtained as scanned PDF files. These were transformed as editable copies by using OCR (=Optical Character Recognition) software Abbyy. The program read the original mid-19<sup>th</sup> century Russian text adequately with its old Russian alphabet package.</p> <p>After the initial corrections, a Python script was written to harvest the personal names. This was based on exploiting the systematic formalities in how most of the names were presented in the census book. The script looked for abbreviations “дв.” and “д.” and extracted all following capitalized words until section end markers “.”, “;” or “:”. As an output, a name to pogost matrix was produced, which held the raw frequencies of each word in each pogost.</p> <p>The process of cleaning the name data, in turn, has been done mostly by data wrangling program OpenRefine in following manner: For starters, all name forms shorter than four characters were removed as there were no personal names consisting of three or less letters. Furthermore, nouns that were not names were removed. This meant discarding expressions that described person’s special feature or profession, like such as being a widow (“вдова”) or working as a deacon (“діакъ”). For some reason, editors followed inconsistent conventions in capitalizing these non-name nouns.</p> <p>In addition, some orthographical and morphological harmonization was done on the data. The letter <em>ы </em>was cut from the end of bynames, where it denotes plurality. Similarity of so called soft and hard signs, <em>ь </em>and <em>ъ</em> caused some problems. As the latter one is not used in contemporary Russian and was not used in the original documents either (Неволин 1853 : 4 (in Appendix 1)) it was removed. The soft sign <em>ь </em>was also removed because it was absent in the original documents and it had been used inconsistently by the editors. The letter <em>ѣ</em> (yat) is rarely used in personal names but nevertheless, it was changed to <em>е </em>(like as it is in contemporary Russian) as since it was often confused with soft and hard signs (<em>ь </em>and <em>ъ</em>). Furthermore, the letter <em>ѳ </em>(fita) was often erroneously recognized as <em>о </em>or <em>е. </em>As it is only found in NPK III and only in the beginning of certain names, which all are also written with “Ф” (e.g. “Ѳедко” vs. “Федко”), it was replaced with <em>Ф</em>.</p> <p>In the second phase most of the erroneous orthographies were corrected. We do not detail herescribe all the OCR-errors here that were found, but in the following a short description is given of the most significant corrections. There were, for example, many letters whose similarity caused problems for the OCR-program (e.g. <em>и </em>/ <em>й </em>and <em>б </em>/ <em>в</em>). In these cases, the correct orthography was sought in the census book editions and accordingly, Openrefine was used to change erroneous forms to right correct ones.</p> <p>After the corrections were made, the number of name types (= name variants) was reduced from 4942 to 2748. The Overall overall number of name tokens was dropped as well: from 36,405 to 35,726. Of the name types, more than half (1484) have only one occurrence.</p> <p>The refined and harmonized data is published as pogost-by-name frequency tabulations (<em>pogost,</em> equivalent of English <em>parish</em>). The file is in tab-delimited file (.tsv) format.</p> <p>References:</p> <p>Неволин, К. А. 1853, О пятинах и погостах новгородских в XVI веке, с приложением карты, Санкт-Петербург (Из Записок Императорского русского географического общества, Кн. VIII).</p> <p>NPK III = Новгородские писцовые книги, Т. 3 : Переписная оброчная книга Вотской пятины, 1500 года, 1868, 1868, Санкт Петербург.</p> <p>POKV I, II = Переписная окладная книга по Новугороду Вотьской пятины, 1851, 1852, Имп. Моск. о-во истории и древностей рос., Москва.</p> <p> </p>
Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)
<p>The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census, originally downloaded from the Australian Bureau of Statistics (<a href="https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July\%20201.">ABS</a>).</p><p>This data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.</p><p>Sainsbury-Dale et al. (2023) used a subset of this data set in a study on poverty levels in an area of (NSW) surrounding Sydney. </p><p> </p><p><strong>References</strong></p><p>Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023) "Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK", <i>Journal of Statistical Software</i>, to appear.</p>
Vascular epiphyte census data at the San Lorenzo Crane plot
<p>Long-term community data of vascular epiphytes on a permanent vegetation study at the San Lorenzo Crane plot. The epiphyte community at the San Lorenzo crane plot on 207 tree individuals was first recorded between 1998 to 2000 and again between 2010 to 2012. Two files are available, one per each census.<br> <br> Headers in each file correspond to:</p> <p><strong>- empty header:</strong> row ID<br> <strong>- treenum: </strong>code of each tree, data taken from the CTFS-Tree Censuses and Inventories in Panama (STRI-CTFS) https://stricollections.org/portal/collections/misc/collprofiles.php?collid=27<br> <strong>- spp_code:</strong> epiphyte species code<br> <strong>- tsppc:</strong> host tree species code<br> <strong>- freq:</strong> number of individuals of epiphyte species</p> <p><br> <br> </p>
NAPS Continuous Data: across 52 Census Divisions from 1980-2019
<p>The National Air Pollution Surveillance (NAPS) Program is a national system of air pollution sampling sites managed by Environment and Climate Change Canada, with cooperation from Provincial, Territorial, and regional partners who manage the sites and report data.</p> <p>This dataset contains aggregated and imputed continuous monitoring pollutant concentrations from 52 Canadian census divisions spanning 1980-2019.</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>
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.
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>
Census-based mobility graph data across 12 U.S. metro regions
<p>This dataset contains mobility information among census tracts for 12 U.S. cities in 2021. Mobility is defined to be the daily commute flow of residents, the data for which is retrieved from the Longitudinal Employer-Household Dynamics (LEHD), a U.S. Census Bureau program. </p> <p>In each of the individual city's directory, the file 'city_network_edges.csv', the 'S000' column the commute flow number between 'origin' and 'destination' census tracts of the city. The geographical boundaries of census tracts are also present in each directory, with 'city_network_nodes' representing shapefiles.</p>
"Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" - calcareous nannofossil census data
<p>This is a data supplement (<strong>Dataset A</strong>) to the paper "Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" by Bordiga et al., 2015a (https://doi:10.5194/cp-11-1249-2015). Note that data are tabulated against depth in core (meters composite depth, mcd). Please refer to <strong>Table 1</strong> in Bordiga et al. (2015) for age-depth model.</p> <p><strong>Dataset A</strong>. Calcareous nannofossil census data (ODP Site 1263)</p> <p>One file (ODP 1263 dataset A_Bordiga et al. 2015.xls) containing:<br> Sample information; Raw counts, relative (%) and absolute abundances (N/ g) of all species and size-based groups detected (as illustrated in Figure S2 of the original publication).</p>
Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama
<p>Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50-ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-yr intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m<sup>2</sup> seedling plot in the center of every 5 x 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m<sup>2</sup> seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1–2 years. Here we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography.</p>
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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.