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3,101 results for “historical”
Snow depth and snow water equivalent measurements along a road course and historic snow course in the Andrews Experimental Forest, 1978 to present
With an increase in emphasis on monitoring climate change impacts and change in the form of precipitation at HJ Andrews Experimental Forest, snow data collection within our climate monitoring program, a snow course to document depths of snow was designed around a dispersed sampling scheme rather than a point intensive scheme as previously employed in the historic Reference Stand snow course. Primary objectives are to document the presence/absence of snow, snow depth, and time of melt-off. Snow depths are verified using stakes placed near the road to allow for routine and frequent observation. Stakes are placed at different locations, elevations and aspects in paired forested/open sites. Time-lapse cameras were deployed at all the stakes to allow for daily measurements beginning in fall 2014. Truthing of points with snow core sampling for snow moisture content (snow water equivalent) is done when possible, usually 1-2 times per year. Cameras are set to take 3 readings per day (09:00, 12:00, 15:00 PST). One snow depth and coverage is extracted from the images per stake per day.
Precipitation measurements from historic and current standard, storage and recording rain gauges at the Andrews Experimental Forest, 1951 to present
Andrews Forest precipitation has been measured continuously using various rain gage types since 1951. Most of these rain gages are standard (non-recording) gages with 7.5 or 8 inch orifices or large capacity storage gages intended for sites with limited access collected irregularly over longer intervals. Recording rain gages have also been established to collect higher temporal resolutions (e.g., 5 minute or 15 minute) and also used as a means of parsing (“prorating”) these periodic interval measurements from these standard and storage gages into daily totals. This data set includes an inventory of all rain gages that have operated within the Andrews as well as one site in the nearby Wildcat RNA and one in the town of Blue River. The inventory includes information regarding the date range of operation, gage location, type of gage, the rain network within which it was established, general availability of data and descriptive notes. A second table includes all of the raw measurement data for these non-recording gages over every interval where data were taken, and additionally includes the corresponding recording gage and its measurement total used to prorate data into a daily record. A third table includes the prorated daily data for all of these standard and storage gages as well as the true daily totals for two recording rain gages. A fourth table includes high temporal resolution for one early recording gage at Forks and the Mack Creek recording gage. Note that while precipitation data associated with the 6 benchmark stations are included in this rain gage inventory (Entity 1), the daily and high temporal resolution data for these sites were available through a separate meteorological data set, database code MS001, until 2025. In 2025, the benchmark station data was migrated here and will be combined with the Forks and Mack Creek data.
Historical GIS Data for Harvard Forest Properties from 1908 to Present
Since 1908, the Harvard Forest has conducted forest surveys approximately every 10-20 years on its three largest tracts (total 1033 ha). These maps have been digitized along with maps of environmental factors (topography, soils), disturbance (1938 hurricane, historical land-use), and silvicultural treatments. These datalayers will allow researchers to understand the influence of environment factors, disturbances, and silviculture on the structure and composition of modern forest stands as well as assisting in locating and describing research sites. The dataset also includes an elevation grid (NED 30 meter cells), and a shapefile of linear features (trails, stonewalls, etc). Original maps were transcribed to standardized basemaps by various researchers. These basemaps were then scanned and digitized as shapefiles in ArcView GIS 3.2. The shapefiles were then transformed to Massachusetts State Plane Meters NAD83 projection in ArcGIS and rubbersheeted to align better with aerial photographs downloaded from MassGIS. Locations of control points will be permanently archived at the Harvard Forest to facilitate transformation of future datalayers.
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).
Regional and Historical Variation in Garlic Mustard Distribution in Western Massachusetts 2006-2007
The susceptibility of a site to invasion by nonnative species depends on its current ecological features and its historical land use. Certain environments might be more conducive to an invasive plant’s success, and several recent studies have shown that former agricultural sites are more susceptible to invasion than sites that have been continuously wooded. We studied the invasive herb garlic mustard (Alliaria petiolata), at roadside forested edges. Site selection was stratified by two regions with distinct ecological characteristics (the Connecticut River Valley and the Berkshire Valley in Massachusetts), and two historical land uses (wooded versus cleared in 1830).
Legacies of Historical Clearcutting, Wind and Salvage Logging in Pisgah State Forest 2016
Disturbance events affect forest composition and structure across a range of spatial and temporal scales, and subsequent forest development may differ after natural, anthropogenic, or compound disturbances. Following large, natural disturbances, salvage logging is a common and often controversial management practice in many regions of the globe. Yet, while the short-term impacts of salvage logging have been studied in many systems, the long-term effects remain unclear. We capitalized on over eighty years of data following an old-growth Tsuga canadensis-Pinus strobus forest in southwestern New Hampshire, USA after the 1938 hurricane, which severely damaged forests across much of New England. To our knowledge, this study provides the longest evaluation of salvage logging impacts, and it highlights developmental trajectories for Tsuga canadensis-Pinus strobus forests under a variety of disturbance histories. Specifically, we examined development from an old-growth condition in 1930 through 2016 across three different disturbance histories: (1) clearcut logging prior to the 1938 hurricane with some subsequent damage by the hurricane (“logged”), (2) severe damage from the 1938 hurricane (“hurricane”), and (3) severe damage from the hurricane followed by salvage logging (“salvaged”). At each site, 2-4 0.05 ha plots were established, and DBH, species, and distance and direction from plot center were recorded for each tree over 10 cm DBH. Stumps and snags were also measured. From the site center, coarse woody material was measured along three 34-m transects using the line-intersect method. Finally, a 1-ha plot was established around the site center, within which all pit-and-mound structures and stumps were recorded as well as boles associated with these structures.
Historical and Ecological GIS Data from Manuel F. Correllus State Forest on Martha’s Vineyard 1830-1994
Sand-plain ecosystems are a priority for conservation because they are uncommon, support numerous rare or uncommon plant and animal species, serve as groundwater recharge areas, and are threatened by land development. The 5,200-acre Manuel F. Correllus State Forest, in the central part of Martha’s Vineyard, is part of one of the larger sand-plain ecosystems in New England. This GIS data package was created as part of a study on the history and ecology of Martha’s Vineyard and the state forest as part of an effort to understand sandplain landscapes and make management recommendations for their maintenance.
Historical GIS Data for Prospect Hill Tract at Harvard Forest 1733-1986
This dataset contains elevation, 1986 forest type, land-use history, and soils maps for the Prospect Hill Tract, digitized from paper maps in the Harvard Forest Archives. File format = Idrisi 4.1 binary. Resolution = 10m x 10m. Coordinates = UTM zone 18. Datum = 1927 North American. This dataset has been replaced with a new vector series for the entire Harvard Forest (see HF110).
Historical and future irrigation water demand for the STARS4Water river basins
<p>Dataset contains data on historical and future irrigation water demand for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average summer net irrigation requirement [mm/year] for each combination GCM model (5 models)/time window (2 windows) was calculated within the boundaries of the project river basin hubs. The difference between the future and historical period was also calculated for each GCM. In addition, ensemble mean values for both horizons and ensemble mean differences were calculated. This dataset was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976).</p>
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide
<p>A complete description of the dataset is given by <a href="http://doi.org/10.1038/s41597-023-02041-1">Jones et al. (2023)</a>. Key information is provided below.</p> <p><strong>Background</strong></p> <p>A dataset describing the global warming response to national emissions CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from fossil and land use sources during 1851-2021.</p> <p>National CO<sub>2 </sub>emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024). </p> <p>National CH<sub>4</sub> and N<sub>2</sub>O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).</p> <p>We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH<sub>4</sub> and N<sub>2</sub>O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).</p> <p>Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).</p> <p>The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or 3-GHG total) and source (fossil emissions, land use emissions or the total).</p> <p><strong>Data records: overview</strong></p> <p>The data records include three comma separated values (.csv) files as described below.</p> <p>All files are in ‘long’ format with one value provided in the <em>Data</em> column for each combination of the categorical variables <em>Year, Country Name, Country ISO3 code, Gas, and Component</em> columns.</p> <p><em>Component</em> specifies fossil emissions, LULUCF emissions or total emissions of the gas.</p> <p><em>Gas</em> specifies CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or the three-gas total (labelled 3-GHG).</p> <p><em>Country ISO3 codes</em> are specifically the unique ISO 3166-1 alpha-3 codes of each country.</p> <p><strong>Data records: specifics</strong></p> <p>Data are provided relative to 2 reference years (denoted <em>ref_year </em>below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.</p> <p><em>EMISSIONS_ANNUAL_{ref_year-20}-2023.csv:</em> <em>Data </em>includes annual emissions of CO<sub>2</sub> (Pg CO<sub>2</sub> year<sup>-1</sup>), CH<sub>4</sub> (Tg CH<sub>4</sub> year<sup>-1</sup>) and N<sub>2</sub>O (Tg N<sub>2</sub>O year<sup>-1</sup>) during the period <em>ref_year-20 </em>to 2023. The <em>Data</em> column provides values for every combination of the categorical variables. Data are provided from <em>ref_year-20</em> because these data are required to calculate GWP* for CH<sub>4</sub>.</p> <p><em>EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data </em>includes the cumulative CO<sub>2</sub> equivalent emissions in units Pg CO<sub>2</sub>-e<sub>100</sub> during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><em>GMST_response_{ref_year+1}-2023.csv:</em> <em>Data</em> includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><strong>Accompanying Code</strong></p> <p>Code is available at: <a href="https://github.com/jonesmattw/National_Warming_Contributions">https://github.com/jonesmattw/National_Warming_Contributions</a> .</p> <p>The code requires Input.zip to run (see README at the GitHub link).</p> <p><strong>Further info: Country Groupings</strong></p> <p>We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:</p> <ul> <li>Annex I countries (number of countries, n = 42)</li> <li>Annex II countries (n = 23)</li> <li>economies in transition (EITs; n = 15)</li> <li>the least developed countries (LDCs; n = 47)</li> <li>the like-minded developing countries (LMDC; n = 24).</li> </ul> <p>And other country groupings:</p> <ul> <li>the organisation for economic co-operation and development (OECD; n = 38)</li> <li>the European Union (EU27 post-Brexit)</li> <li>the Brazil, South Africa, India and China (BASIC) group.</li> </ul> <p>See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.</p>
Forest Type Maps for New England from Historical Studies 1912-1956
This data package contains 3 GIS layers showing generalized forest types across New England as delineated in older forestry publications. These were digitized so that they can be used to illustrate broad vegetation patterns across the region in modern publications. These GIS layers include maps drawn by Hawley and Hawes (1912), RT Fisher (1933), and Westveld and the Committee on Silviculture, New England Section, Society of American Foresters (1956).
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Historical Plat Maps of Dane County Digitized and Converted to GIS (1962-2005)
We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
EMU Historical: member state positions on fiscal reforms, 1992-2010
<p>The EMU Historical dataset reports the positions of EU member states on 44 contested issues related to economic and fiscal reforms between 1992 and 2010. Interactive data portal available at <a href="https://emuchoices.eu/data/emuh/">EMUchoices.eu/data/emuh/</a>. Data are based on existing academic literature and primary documents from the Secretariat of the Council. The dataset was compiled by the EMU Choices consortium, which has received funding from the European Union’s Horizon research and innovation programme under grant agreement No. 649532.</p>
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
Cross-site comparison of historical trends in marsh change at three LTER sites: GCE, VCR, and PIE.
The data provided here are shapefiles which were generated from digitizing historical maps and aerial photographs for a cross site comparison of changes in marshes at the Plum Island Ecosystem (PIE), Virginia Coast Reserve (VCR), and Georgia Coastal Ecosystems (GCE) Long Term Ecological Research (LTER) sites. This dataset includes shoreline shapefiles digitized for 3 time intervals at each site: 1930s/40s, 1950s/70s, and 2013 as well as a table of results from the shoreline change analysis. Centerlines for all the channels were generated from the shoreline shapefiles and are attributed with Strahler (1965) channel order and sinuosity. Additionally, this dataset includes shapefiles of important features (channels, upland, ponds, tidal flats) digitized for each of three sites at three time intervals.
Historical Birge - Juday Lake Survey - major ions 1900 - 1943
Data collected by Birge, Juday, and collaborators, mostly in north-central Wisconsin, from 1900 through 1943; generally one sampling event per lake during the summer, but on some lakes, especially around Trout Lake Station, several sampling events for several successive years. This data set contains both surface data (depth of zero) and multi-depth data. Note that not all variables were measured on all lakes. Documentation: Johnson, M.D. (1984) Documentation and quality assurance of the computer files of historical water chemistry data from the Wisconsin Northern Highland Lake District (the Birge and Juday data).Wisconsin DNR Technical Report. Note: Values of -99999 in water quality data indicate trace amount of parameter was present. Number of sites: 663 (generally one sampling point per lake; occasionally, several sampling points per lake on multibasin, large lakes). Note: This data set was updated in 2013 to include multi-depth and additional surface data for a large subset of lakes. These additions expanded the number of sites from 605 to 663, and expanded the date range from 1925-1942 to 1900-1943 . Furthermore, 14 lakes in Minnesota were added to the data set contributing additional surface and multi-depth data. Another dataset was added in 2013 collected by Wisconsin limnologists Chauncey Juday and Edward Birge, this data set contains variables that are still commonly used in research. For example, temperature, dissolved carbon dioxide, color, pH, secchi disk, plankton, and silica. However, the data set also includes variables that are not commonly used, for example, crude protein, non-amino nitrogen, ether extract, and total organic and inorganic material. These data are characteristic of water chemistry analysis from the time in which they were compiled (5/31/1915 - 8/29/1938). The data set features data from 586 different lakes, primarily lakes in the Northern Highland Lakes District of Wisconsin. However, there is also data from lakes in southeastern and
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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.