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12 results for “inventory survey”
Inventory data of woody plants surveyed and measured in North Senegal (Ferlo) in 2015-2017
<p>This dataset gathers measurements from field inventory on woody vegetation carried in the sylvo-pastoral zone of Ferlo (Senegalese Sahel) in 2015-2016-2017. The data consist of dendrometric measurements, location and species for 3215 woody individuals (trees, bushes, and shrubs) belonging to 25 species and 11 families.</p> <p><strong>Sites </strong></p> <p>The study sites are located in the Northern Sandy Pastoral Region of Senegal around the deep wells of Widou Thiengoly (15.99° N, 15.32° W) and Tessékéré (15.85° N, 15.06° W). The vegetation formation is an open savanna, with a relatively low woody cover.</p> <p>For the <strong>field work of 2015</strong>, we applied a stratified sampling according to the topography and the distance to the studied deep wells. We inventoried 139 plots of 0.25 ha each. The center of each plot was marked by a gps point. The plots were located at increasing distances from the boreholes: 2, 3.5, 5, 7.5, 10, 12.5 and 15 km (20 plots per distance). For each distance, we randomly selected at least six plots in depressions (43 plots in total), the other plots being located on slopes or on hilltops, with a total vertical drop of several meters (96 plots in total). Whenever possible, the plots in each category of topography were distributed between the two soil types. In total, 86 plots were allocated to the ferruginous soils and 53 plots to the sub-arid brown red soils. </p> <p>In<strong> 2016, </strong>additional woody plants were surveyed within 10 circular plots with a variable radius between 27 m and 52 m, so that at least 10 individuals were counted for each plot. <strong>In 2017, </strong>woody plants were surveyed within 30 square plots of 0.25 ha each. Woody individuals were all geotagged in 2016 and 2017. </p> <p><strong>Field measurements</strong></p> <p>Adults woody plants (with a circumference superior to 10 cm at ground level) were inventoried within square plots (2015, 2017) or circular (2016). Species name was identified for all individuals and recorded following the taxonomic referential of the African Plant Database (version 3.4.0). Three types of dendrometric measurements were performed on woody plants: (i) circumference, measured at 30 cm from ground level, except for shrubs for which circumference was measured at ground level; (ii) tree height, measured by an ultrasonic hypsometer Vertex IV (Haglof Inc.) (iii) two perpendicular crown diameters. GPS points were taken (GPSMAP 62, Garmin Inc.) at the center of each plot (for 2015) and, in some cases for each individual (2016-2017).</p> <p><strong>Data structure and metadata</strong></p> <p>Data are encoded in a single file, using comma-delimited format and UTF-8 encoding. Each row describes one individual woody plant with its corresponding measurements. The following table presents the variables (columns) contained in the dataset.</p> <p>Shapefile format is also available (same data as the .csv).</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> </tr> <tr> <td> <p>Tree_id</p> </td> <td> <p>-</p> </td> <td> <p>Unique identifier of the individual, with a 13 characters length. The 4 characters following the “y” indicate the year of the inventory. For instance, “tr.y2015.0034” is referring to the woody plant number 34 inventoried in 2015.</p> </td> </tr> <tr> <td> <p>Plot_id</p> </td> <td> <p>-</p> </td> <td> <p>Plot identifier</p> </td> </tr> <tr> <td> <p>Plot_area_ha</p> </td> <td> <p>ha</p> </td> <td> <p>Area of the inventoried plot</p> </td> </tr> <tr> <td> <p>Species</p> </td> <td> <p>-</p> </td> <td> <p>Genus, species, subspecies names and botanical authors</p> </td> </tr> <tr> <td> <p>Family</p> </td> <td> <p>-</p> </td> <td> <p>Family name</p> </td> </tr> <tr> <td> <p>Growth_form</p> </td> <td> <p>-</p> </td> <td> <p>Shrub, bush or tree</p> <p>Growth form expresses the extent of growth and the potential branching of the main-shoot axis. In this work, we refer to three types of growth form: shrub, bush and tree. A shrub refers to a small woody plant with a height below 2 meters and multi-stemmed. A tree designates a woody plant taller than 5 to 6 meters, generally presenting a single trunk. A bush, or a dwarf tree as in Pérez-Harguindeguy et al. (2013), is the intermediary between a shrub and a tree. Its height is usually between 2 to 6 meters and it is often multi-stemmed. Because of intra-specific traits variation, the mentioned growth form is valid for our study area.</p> </td> </tr> <tr> <td> <p>Circ30_m</p> </td> <td> <p>m</p> </td> <td> <p>Circumference measured at 30 cm from ground level. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Height_m</p> </td> <td> <p>m</p> </td> <td> <p>Tree height. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Dcrown1_m</p> </td> <td> <p>m</p> </td> <td> <p>First diameter of the crown</p> </td> </tr> <tr> <td> <p>Dcrown2_m</p> </td> <td> <p>m</p> </td> <td> <p>Second diameter of the crown (perpendicular to the first diameter)</p> </td> </tr> <tr> <td> <p>Geoloc_method</p> </td> <td> <p>-</p> </td> <td> <p>Geolocation method; indicates if it is the center of the plot which was geolocated (“geoloc.plot”, for individuals in 2015) or the woody plant (“geoloc.tree”, for 2016-2017).</p> </td> </tr> <tr> <td> <p>Lat_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>North latitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geoloc.tree”.</p> </td> </tr> <tr> <td> <p>Long_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>West longitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geloc.tree”.</p> </td> </tr> <tr> <td> <p>Topography</p> </td> <td> <p>-</p> </td> <td> <p>Local topography of the plot. Indicates if the plot is located within a depression (lowland) or on a hilltop.</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>-</p> </td> <td> <p>Date of the survey: dd-mm-yy</p> </td> </tr> </tbody> </table> <p> </p>
Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)
<p>Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1255196">http://doi.org/10.5281/zenodo.1255196</a>.)</p> <p>At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. </p> <p>In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.</p> <p>Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.</p>
CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
<p>This dataset contains the forest inventory survey data as aqcuired by airborne imaging spectroscopy (i.e., continuum removed spectral reflectance) and field inventory surveys (i.e., relative species abundance) for 65 field plots along a temperate-to-boreal forest gradient in southern Québec, Canada. Additionally, it contains plot-level average environmental variables (e.g., elevation, slope, etc.) and species-mean foliar traits. This data supports the manuscript titled "Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis" <em>in press </em>at <em>Ecological Monographs </em>(ID: ECM23-0177) and accompanying code (Crofts 2024) is archieved on Zenodo (<a href="https://doi.org/10.5281/zenodo.10735410)">doi: 10.5281/zenodo.10735410)</a>. Please refer to the methods section of the associated paper for methodological details, with additional details available at protocols.io (<a href="https://doi.org/10.17504/protocols.io.q26g7rn23vwz/v2">doi: 10.17504/protocols.io.q26g7rn23vwz/v2).</a> This data was aqcuired as a part of the Canadian Airborne Biodiversity Observatory (CABO; caboscience.org) and has the licence Creative Commons by Attribution (cc-by). Note: this dataset is also published of EcoSiS (<a href="https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)">https://ecosis.org/package/cabo-canopy-level-spectra-from-forest-sites)</a></p>
FIG. 3 in A biotic survey and inventory of the dynastine scarab beetles of Mesoamerica, North America, and the West Indies: review of a long-term, multicountry project
FIG. 3. — Map of the study area showing the phases (I-IV) of the dynastine biotic inventory project.
FIG. 1 in A biotic survey and inventory of the dynastine scarab beetles of Mesoamerica, North America, and the West Indies: review of a long-term, multicountry project
FIG. 1. — Brett Ratcliffe and Renaud Paulian in Bordeaux, France, 13 May 2001. Photo: M. L. Jameson.
CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
Open the record for dataset details and reuse information.
Barrow Nasal Inventory Survey
ClinicalTrials.gov study NCT01322945. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Figure 2 in Assessing the efficacy of higher-taxon approach for ant species surveys to improve biodiversity inventories
Figure 2 Distributions of regression and correlation coefficients between genera- and species-level taxonomy for taxon richness (a, b) and composition (c, d) and their correlations with the environment, or % clay, slope, vegetation type and locations (e, f) by decade over 120 years for both the complete (left) and reduced (right) datasets. Smooth curves have been fitted by LOESS and their respective 95% confidence interval (shaded area) for the correlation coefficients over decades are shown.
Figure 3 in Assessing the efficacy of higher-taxon approach for ant species surveys to improve biodiversity inventories
Figure 3 Relationships between matrix regression and correlation coefficients and the number of ant species per genus measured at fine spatial scale for taxon richness (a, b) and composition (c, d) and their correlations with the environment (e, f) for the complete (left) and reduced (right) datasets.
Figure 1 in Assessing the efficacy of higher-taxon approach for ant species surveys to improve biodiversity inventories
Figure 1 Regressions between the numbers of genera and species of ground-dwelling ants for complete (red circles) and reduced (green circles) database per plot, for nine study sites located in the Brazilian Amazon. The statistical significance of models was tested using 1000 permutations. Allregressionsweresignificantat P <0.001.
Development of the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS):An Exploratory Mixed-method Study
ClinicalTrials.gov study NCT04833803. IPD Sharing: NO. Countries: 0. Publications: 2.
FIG. 2 in A biotic survey and inventory of the dynastine scarab beetles of Mesoamerica, North America, and the West Indies: review of a long-term, multicountry project
FIG. 2. — The many forms and sizes of Dynastinae. Photo: M. L. Jameson.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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