Find research datasets worth reusing

Cooperative Alaska Forest Inventory (CAFI): I - Tree Inventory Data 1994-2024

The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the tree data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.

Cooperative Alaska Forest Inventory (CAFI): II - Seedling Inventory Data 1994-2024

The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the seedling data of the CAFI. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.

Cooperative Alaska Forest Inventory (CAFI): III - Vegetation Data 1994-2024

The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the vegetation data of the CAFI. The protocol has been changed in 2021. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.

Tree Inventories for Validating Terrestrial Lidar Measurements at Harvard Forest 2007-2014

Our objective is to improve the measurements of canopy structure and biomass of a forest stand and detect their annual changes via a ground-based laser scanning technology, also known as terrestrial lidar (TLS). A TLS instrument utilizes lasers to scan an environment, measure 3D locations of objects encountered by lasers and detect intensities of laser lights scattered by those objects back to the TLS instrument. TLS have shown abilities and is being further explored to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, aboveground biomass and other useful forest structural parameters rapidly and accurately. Three TLS instruments used in this project include: (1) the Echidna (R) Validation Instrument (EVI), built by CSIRO Australia; (2) Dual-Wavelength Echidna® Lidar (DWEL), built by Boston University, University of Massachusetts, Lowell, University of Massachusetts, Boston and CSIRO Australia; (3) Compact Biomass Lidar (CBL), built by University of Massachusetts, Boston. To validate the forest structural parameters retrieved using these TLS instruments, we set up a one-ha (100 m by 100 m) forest site and collected tree inventory data including: tree location, tree species, DBH, tree height and crown dimension since 2007 with a two-year gap of 2008 and 2009. Lidar data are available from the ORNL DAAC (http://dx.doi.org/10.3334/ORNLDAAC/1045).

Vegetation Inventory of Harvard Forest 1986-1993

The three main tracts of the Harvard Forest (3000 acres) in Petersham MA have been sampled every 10-30 years since 1907. Though methods have varied, each survey has involved mapping forest stands followed by intensive sampling. This inventory was completed in 1986-1993.

openCC0Jun 2025View details →

Biomass Inventories at Harvard Forest EMS Tower since 1993

In 1993, we installed 40 circular, 10 m radius biometric plots in the footprint of the EMS tower on Prospect Hill. We randomly placed the plots within 100 m increments along ten 500 m transects that extend from the tower in the northwest and southwest directions. In 2001, we removed three plots (G3, H3, H4) from the datasets and ceased measurements there due to their inundation by a beaver pond. In 1999, we installed 6 additional circular, 10 m radius biometric plots on the Simes Lot, adjacent to Prospect Hill to study the effects of a selective harvest that occurred there in the winter of 2000-01. In the summer of 2001, we expanded the harvested plots in size to 15 m radius and ceased measurements at one plot (X4) because it was unaffected by the harvest. The harvest also affected three of the original tower plots (A4, A5, B5), which were expanded in size as a part of the harvest plot group. Consequently, there are 34 tower plots and 8 harvest plots. We have taken the following ecological measurements at each site: tree growth, woody debris, litter, leaf area increment (LAI), leaf chemistry, and soil respiration and moisture.

Inventory of Ants at the Black Rock Forest in Cornwall NY 2006-2015

Ants are key indicators of ecological change, but few studies have investigated how ant assemblages may respond to dramatic changes in vegetation structure in temperate forests. Pests and pathogens are causing widespread loss of dominant canopy tree species; ant species composition and abundance may be very sensitive to such losses. Prior to the experimental removal of red oak trees to simulate effects of sudden oak death and examine the long-term impact of oak loss at the Black Rock Forest (Cornwall, New York), we carried out a rapid assessment of the ant assemblage in the 10-hectare experimental area. We also determined the efficacy in a northern temperate forest of five different collecting methods - pitfall traps, litter samples, tuna-fish and cookie baits, and hand collection - routinely used to sample ants in tropical systems. A total of 33 species in 14 genera were collected and identified; the myrmecines Aphaenogaster rudis and Myrmica punctiventris, and the formicine Formica neogagates were the most common and abundant species encountered. Ninety-four percent (31 of 33) of the species were collected by litter sampling and structured hand sampling together, and we conclude that in combination, these two methods are sufficient to assess species richness and composition of ant assemblages in northern temperate forests. Using new, unbiased estimators, we project that 38-58 ant species are likely to occur at Black Rock Forest. Loss of oak from these forests may favor Camponotus species that nest in decomposing wood and open-habitat specialists in the genus Lasius.

openCC0Nov 2022View details →

Long-term dynamics of tropical rain forests in permanent inventory plots, La Selva, Costa Rica (1969-1995)

Three permanent plots comprising a total of 12.4 ha were established in 1969 in tropical rain forest at La Selva Biological Station, near Puerto Viejo de Sarapiquí, in the Caribbean lowlands of Costa Rica. The plots were established in old-growth forest on three contrasting landforms: Plot 1 (4.4 ha) on old alluvial terrace; Plot 2 (4.0 ha) in swamp forest and rolling hills; and Plot 3 (4.0 ha) on steeply dissected terrain with residual soils. The data archived here include plot inventories carried out at five census dates over a period of 27 years. The inventory starting dates were 1969; 1982; 1985; 1989; and 1995. All stems 10 cm dbh or greater were tagged with a permanent numbered tag; measured in diameter at breast height and above buttresses to the nearest mm; mapped on the ground to the nearest m; and identified to species. At each census, live trees were re-measured, dead trees were recorded along with information on the manner of death, other details on the condition of the tree were noted, and new recruits were tagged, mapped, measured, and identified. The archived data include these five components: (1) The master data file, including comprehensive data on all tagged individuals in the three plots for the five censuses from 1969-1995. Each line in the data set represents an individual tagged tree or liana. The data array comprises 8689 lines (the number of tagged individuals) x 48 columns of data. The lines in the data set are ordered first by Plot number (1, 2, 3); next by subplot within each plot; and then by tag number within each subplot. (2) A list of column identifiers, describing in detail the information represented in each of the 48 columns within the master data file. The list gives a description of the data in each column, the units of measurement, and a guide to the interpretation of zeroes in the data. (3) A key to codes used in the field to describe the condition of individual trees. (4) A taxonomic reference list, including all species found

Cooperative Alaska Forest Inventory (CAFI): IV - Sapling Inventory Data 2022-2024

The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the sapling data of the CAFI. Sapling data is only available after 2022 due to a protocol change. The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today.

Cooperative Alaska Forest Inventory (CAFI): V - Photo Collection 2022-2024

Forest Inventory for Tree Demography and Carbohydrate Reserves at Harvard Forest 2009-2011

The objective of this study is to establish a network of forest plots spanning the eastern U.S. focused on understanding how regional scale climate patterns affect patterns of tree demography (e.g. growth, mortality, dieback). Within each research site we also aim to understand the landscape-scale variability in demographic rates and how these rates are affected by edaphic variables by aligning plots along primary environmental gradients (elevation, soils, hydrology, fire return interval). The demographic data obtained will also be compared to regional and landscape-scale patterns in carbon reserves in adults and sapling by sampling root and stem concentrations of nonstructural carbohydrates (TNC) and relate these to demographic patterns, life-history traits, and plant C:N ratios. Besides addressing important ecological questions directly, this study is designed to improve the representations of vegetation dynamics and carbohydrate reserves in regional and landscape-scale forest ecosystem models--two of the least data-constrained processes in such models--by parameterizing and validating the modules for these processes in the Ecosystem Demography model (ED v2.1). This data set contains two years of census data for eight mapped plots distributed across Harvard Forest along the aforementioned primary environmental gradients.

Vegetation Inventory of Harvard Forest 1937

The three main tracts of the Harvard Forest (3000 acres) in Petersham, MA have been sampled every 10-30 years since 1907. Though methods have varied, each survey has involved mapping forest stands followed by intensive sampling. The 1937 forest inventory was conducted one year before 75% of the standing timber at Harvard Forest was blown down by the 1938 hurricane. This valuable data set shows maximum vegetation development since agricultural abandonment. Data collected in this inventory include tree volume by species and presence/absence of advance regeneration, shrubs, herbs and bryophytes.

openCC (other)Jul 2023View details →

Minneapolis-St. Paul Urban Tree Inventory

This dataset is a compilation of spatially explicit, species-specific urban tree inventories from across the seven-county Minneapolis-St. Paul (MSP) metropolitan area in Minnesota, U.S.A. The dataset was compiled to examine fine-scale patterns of tree biodiversity across MSP. Existing tree inventories were solicited from all municipalities, counties, park systems, and relevant non-profit organizations in the region for which we were able to find contact information, resulting in inventories from 35 municipalities, one county, one park system, three non-profit organizations and and two prior academic research efforts. The spatial and temporal scope of the inventories varies; for example, the inventories from some municipalities include data from a subset of only street trees at one timepoint, while other municipal inventories were continuously updated datasets with spatially comprehensive data for street trees in addition to some trees in parks and private lands. No inventory was fully comprehensive of all trees in an area. Data are assumed to have been collected between 2012-2022, although the timestamp on each data point is not explicit. Individual inventories were combined into one uniform database.

opencc-by-4.0Nov 2024View details →

Harmonized IACS inventory

<h2>Inventory description</h2> The Harmonized IACS inventory of Europe-LAND is a harmonised collection of data from the Geospatial Aid (GSA) system of the Integrated Control and Administration System (IACS), which manages and controls agricultural subsidies in the European Union (EU). The GSA data are a unique data source with field-levels of land use information that are annually generated. The data carry information on crops grown per field, a unique identifier of the subsidy applicants that allows to aggregate fields to farms, and information on organic cultivation. The inventory contains all data that can be shared following the General Data Protection Regulations (GDPR) of the data providers. It covers 19 EU member states with time series up to 17 years. For most members states, only the crop information can be shared. However, for six member states also the farm identifier (Czechia, Denmark, Estonia, Ireland, Portugal and Spain) and for five also the organic management information (Austria, Flanders in Belgium, Denmark, Ireland, and Bulgaria) can be shared.  Due to General Data Protection Regulations (GDPR), we are not allowed to share all data that we collected and harmonised. We hold GSA data for six additional member states (Italy, Greece, Poland, Hungary, Romania, and Cyprus) as well as supplementary information on farm-level indicators for 17 additional member states, federal states, or regions (Austria, Cyprus, Greece, Latvia, Netherlands, Romania, Sweden, Slovenia, Slovakia, Wallonia in Belgium, Brandenburg, Lower Saxony, Saxony-Anhalt, and Thuringia in Germany, and Emilia-Romagna, Marche, and Toscana in Italy,) and organic farming information for seven more member states, federal states, or regions (Greece, Netherlands, Sweden, Slovenia, Slovakia, Wallonia in Belgium, and Brandenburg, Lower Saxony, Saarland, Saxony-Anhalt, and Thuringia in Germany). For Luxembourg and Malta, only LPIS data were available. As these datasets contain only reference parcels without detailed land-use information at the parcel level, they were not included in the inventory.  If you use the data, please also cite the original sources of the data. You can find the references in the documentation provided in the "_Documentation.zip".  The crop information were harmonised using the Hierarchical Crop and Agriculture Taxonomy (HCAT) of the <a href="https://zenodo.org/records/14094196" target="_blank" rel="noopener">EuroCrops</a> project (<a href="https://doi.org/10.1038/s41597-023-02517-0" target="_blank" rel="noopener">Schneider et al., 2023</a>). To allow for interoperability with EuroCrops, the harmonised Europe-LAND data come with the same column names that relate to the crop information. All crop mapping tables can be found in our <a href="https://github.com/clejae/europe_land_iacs_prep">GitHub repository</a>. <h3>Column names:</h3> <ul> <li>field_id (mandatory): Unique identifier for each parcel per member state, state, or region</li> <li>farm_id (optional): Unique identifier for each farm per member state, state, or region</li> <li>crop_code (mandatory): Original, member state-specific crop code</li> <li>crop_name (mandatory): Original, member state-specific crop name</li> <li>EC_trans_n (mandatory): Original crop name translated into English</li> <li>EC_hcat_n (mandatory): Machine-readable HCAT name of the crop</li> <li>EC_hcat_c (mandatory): The 10-digit HCAT code indicating the hierarchy of the crop</li> <li>organic (optional): Whether a parcel was conventional (0), organic (1), or is in the conversion process to organic cultivation (2)</li> <li>field_size (mandatory): Size of parcel/reference parcel in hectares</li> <li>crop_area (optional): Area in hectares of the main crop reported in crop column. The crop_area column only occurs if multiple crops are reported per reference parcel.</li> </ul> More detailed information for all members states in our harmonised inventory can also be found in the documentation.  <div> The inventory will be updated at least annually. We will update as additional data becomes available and as new versions of the HCAT are released. Moreover, in future versions, we will add new data on information on agri-environmental measures, eco-schemes, and animal numbers per farm.   </div> <h2>Information on data provision</h2> All files come as .geoparquets to stay within the space limitations of Zenodo. Geoparquets can simply be opened in QGIS via drag and drop. Additionally, various libraries from different porgramming languages are able to handle geoparquets, e.g. geoarrow and sgarrwo in R, GDAL/OGR in C++, GeoParquet.jl in Julia or Fiona in Python.  <div> We bundled multiple years of each member state to stay below the file number limitation of Zenodo. Each zip file name indicates the member state, federal state, or region and the years covered. The meaning of the abbreviations of the members states, federal states, and regions can be found in the "country_region_codes.xlsx" in the "_Documentation.zip".   </div> <div> The Spanish data are also bundled across regions, as they are separated into 50 regions. See the country_regions_codes.xlsx tables for the meaning of the abbreviations:  </div> <ul> <li>ES_Bundle1 (Northeast): BAL, BAR, CAS, GIR, HEC, LLE, NAV, TAR, TER, ZAR</li> <li>ES_Bundel2 (Northwest): ACO, ALA, AST, BUR, CAN, GUI, LEO, LRI, LUG, OUR, PAL, PON, VIZ, VLD, ZAM</li> <li>ES_Bundle3 (West): ALB, AVI, CAC, CIU, CUE, GUA, MAD, SAL, SEG, SOR, TOL</li> <li>ES_Bundle4 (Southwest): ALI, ALM, BAD, CAD, CDB, GRA, HEV, JAE, LAP, MAL, MUR, SAN, SEV, VLC</li> </ul> <h2>Changelog</h2> Version 1.2: In this version, we have corrected the inventory description and documentation to only refer to data that we share publicly. Additionally, we corrected an error in the Spanish data, where the crop_code column got mixed up during pre-processing. This did not affect the other columns, which were correct. Moreover, we have uploaded new data for Bulgaria to the inventory.  Version 1.1: In this version, we corrected some data errors that occured in v1 due to a failure of our quality checks. First, not all fields got classified in v1, and secondly, there were two different datatypes in the EC_hcat_c in many files. Both errors are now corrected.

opencc-by-4.0Aug 2022View details →

DESIRA - inventory of digital tools for agriculture, forestry, and rural areas

Inventory of digital tools for agriculture, forestry, and rural areas collected by the DESIRA consortium.

opencc-by-4.0Jul 2024View details →

Antarctic Ecosystem Inventory: Spatial data for Ice-free lands v1.0

This is Antarctica’s first comprehensive ecosystem map of ice-free lands. The data comprise a spatially explicit 3-tiered hierarchical ecosystem classification with nine Major Environment Types (tier 1), 33 Habitat Complexes (tier 2) and 269 Bioregional Ecosystem Types (tier 3). These Bioregional Ecosystem Types are aligned with ‘level 4’ of the IUCN Global Ecosystem Typology (Keith et al. 2022).  The spatial data are available in raster format (TIF) at 100 m resolution in the Polar Stereographic Projected Coordinate System (GCS_WGS_1984) for all known ice-free areas south from latitude -57.330551 decimal degrees South (pdf map shows extent of ice-free areas in relation to terrestrial ice and ice shelves). A value attribute table (VAT) provides text fields containing codes and full names for each unit in each level of the classification hierarchy and the spatial extent of tier 3 units in hectares. Methods of development, source data and uses of the inventory are detailed by Tóth et al. (2025a). Descriptive profiles for tier 1 and 2 units are available in Tóth et al. (2025b). References Keith, D.A., Ferrer-Paris, J.R., Nicholson, E., Bishop, M.J., Polidoro, B.A., Ramirez-Llodra, E., Tozer, M.G., Nel, J.L., Nally, R. Mac, Gregr, E.J., Watermeyer, K.E., Essl, F., Faber-Langendoen, D., Franklin, J., Lehmann, C.E.R., Etter, A., Roux, D.J., Stark, J.S., Rowland, J.A., Brummitt, N.A., Fernandez-Arcaya, U.C., Suthers, I.M., Wiser, S.K., Donohue, I., Jackson, L.J., Pennington, R.T., Iliffe, T.M., Gerovasileiou, V., Giller, P., Robson, B.J., Pettorelli, N., Andrade, A., Lindgaard, A., Tahvanainen, T., Terauds, A., Chadwick, M.A., Murray, N.J., Moat, J., Pliscoff, P., Zager, I. & Kingsford, R.T. (2022) A function-based typology for Earth’s ecosystems. Nature 610, 513–518. [doi: 10.1038/s41586-022-05318-4]. Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. and Keith D.A. (2025a). A dataset of Antarctic ecosystems in ice-free lands: classification, descriptions, and maps. Scientific Data 12, 133. [https://doi.org/10.1038/s41597-025-04424-y]  Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. & Keith D.A. (2025b). Antarctic Ecosystem Inventory: Descriptive profiles for ice-free lands v1.0. DOI: 110.5281/zenodo.14625890. Australian Antarctic Data Centre.

opencc-by-4.0Mar 2018View details →

EU MarcoPolo project | SO2 emission inventory over China

The aposteriori SO2 emissions for year 2014, in the domain from 102°E to 132°E and from 15°N to 55°N, in a 0.25°x0.25° spatial resolution and monthly temporal resolution, have been provided to the MarcoPolo project and can be found at <a href="http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/">http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/</a>. For details on the creation of the inventory refer to <a href="http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf">http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf</a> and for the inclusion of the SO2 emission inventory to the MarcoPolo Emission Database refer to: <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf</a> as well as <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf</a> . The main reference to this dataset is found here: Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.: Updated SO2 emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, https://doi.org/10.5194/amt-11-1817-2018, 2018. The netcdf data files contain the following structure: <ul> <li>Dimensions <ul> <li>lat = 129</li> <li>lon = 121</li> </ul> </li> <li>Attributes <ul> <li>author = "MariLiza Koukouli"</li> <li>contact information = "mariliza@auth.gr"</li> <li>institution = "Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki"</li> <li>time frame = "2014"</li> <li>sector classification = "total emissions"</li> <li>emis_cat_name = "sulphur dioxide emissions"</li> <li>source_type_name = "sulphur dioxide emissions"</li> <li>pollutant_description = "updated sulphur dioxide emissions based on the CHIMERE model running the MEIC emissions and the OMI/Aura observations"</li> <li>unit_emissions = "Mg/month"</li> <li>nodata_value = "-9999.0"</li> </ul> </li> <li>Variables <ul> <li>float emissions(lon, lat)</li> </ul> </li> </ul>

opencc-by-4.0Nov 2024View details →

AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots

This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, images.zip, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process. The second archive, videos_app_HR.zip, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization. The third archive, videos_app_TM.zip, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers. In addition to the visual data, the dataset includes a comma-separated values (CSV) file named information_all_trees.csv, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations. The column headers in information_all_trees.csv are as follows: <ul> <li>PLOT_ID: The numeric identifier for each sample plot.</li> <li>tree_species_short: Abbreviation of the tree species.</li> <li>caliper_dbh: The manually measured dbh of the tree in centimeters.</li> <li>AVP_App1_dbh: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li>AVP_App2_dbh: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li>res_App1: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li>res_App2: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li>tree_species: The Latin name of the tree species, with genus and species connected by an "_".</li> <li>tree_class: Classification of the tree into a species-specific category.</li> <li>date: The date of the recordings.</li> <li>time_App_1_min: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li>time_App_2_min: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li>time_manual_caliper_min: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li>measuring_person: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li>mean_slope_degrees: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.

opencc-by-4.0Jan 2023View details →

Syrian Migration to Europe, 2011-21: Data Inventory

This inventory includes metadata on various quantitative and qualitative sources of information on Syrian migration to Europe in 2011-21 that can be used for agent-based modelling purposes, with each source accompanied by data quality assessment. The files are available in a TSV and MS Excel format. The judgement-based quality ratings provided are specific to the requirements of agent-based modelling, as detailed in the <a href="https://www.baps-project.eu/inventory/project_outputs/data_sources/Background%20paper%20Data%20and%20knowledge.pdf">background paper.</a> A queryable version of the inventory is available on the website of the project Bayesian Agent-Based Population Studies (BAPS), funded by the European Research Council (725232): <a href="https://baps-project.eu/inventory/data_inventory">https://baps-project.eu/inventory/data_inventory</a>. The methodology behind assembling this dataset and assessing the individual data sources according to pre-defined quality criteria is detailed in: Nurse S and Bijak J (2022) Building a Knowledge Base for the Model. In: J Bijak et al., Towards Bayesian Model-Based Demography. Agency, Complexity and Uncertainty in Migration Studies. Methodos Series, vol 17. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-83039-7_4">https://doi.org/10.1007/978-3-030-83039-7_4</a>