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389 results for “woodlands”
Data from: Exploring the biotic homogenisation and diversity resistance hypotheses: the understorey of non-native and native woodland canopies in three urban areas of Europe
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Data from: Woodland ectomycorrhizal fungi benefit from large-scale reduction of nitrogen deposition in the Netherlands
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Western Juniper Woodlands climate relations meta analysis
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Water availability drives fine root dynamics in a Eucalyptus woodland under elevated atmospheric CO2 concentration
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Thermodynamics of soil organic matter decomposition in semi-natural oak (Quercus) woodland in southwest Ireland
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Survival and home ranges of woodland birds in restoration plantings
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Fire and functional traits: using functional groups of birds and plants to guide management in fire-prone, heathy woodland ecosystem
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NPP Multi-Biome: Global IBP Woodlands Data, 1955-1975, R1
This data set provides four data files containing net primary productivity (NPP) data, edaphic characteristics, average climatic conditions, and basic descriptive and quantitative information on vegetation for 117 globally-distributed terrestrial forest sites. The data set was derived from the IBP (International Biological Programme) Woodlands Data Set of DeAngelis et al. (1981). The data were collected from the mid 1950s to the early 1970s and were compiled into an electronic data set at the Oak Ridge National Laboratory to facilitate comparisons involving the diverse woodland ecosystems. One data file provides a complete synthesis of NPP, vegetation, edaphic, and climate data and data-source references for each of the 117 sites as published in DeAngelis et al. (1981) for a total of 5,887 records. The second file provides site location, biome, and selected forest productivity and soils data for the 117 sites. The third file provides summary climate data (temperature, precipitation, radiation, growing season length) for each site, and the fourth file provides forest type, investigator(s), and years of the study for each site. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally archived in 1997 (DeAngelis, et al, 1997.)
FR094, Early Woodland, Ovate Stemmed Adena
Ovate Stemmed Adena Early Woodland Catalog #: P545 Uploaded by Gwyneth Harris Suggested Data Citation: Thompson, Christine, Erin Powers, Gwyneth Harris, and Kevin C. Nolan, 2021. FRHS_FR094, 3D Model .ply file. Digital Exhibit of Fort Recovery Historical Society's Precontact Collection, Fort Recovery Historical Society and Applied Anthropology Laboratories, Ball State University. Source: Objaverse 1.0 / Sketchfab
Effect of resource abundance on woodland rodents' demography at latitudinal extremes in Europe
<p>This pre-released repository holds the data and codes from the project <em>Effect of resource abundance on woodland rodents’ demography at latitudinal extremes in Europe.</em></p>
Fig. 3 in Effect Of Farming And Rainfall On The Species Diversity, Population Density And Community Structure Of Birds Breeding In The Kalahari Woodland, Ne Namibia
Fig. 3. Percentage of main feeding guilds in 2014 and 2015.
FR053, Early Woodland, ovate blade adena
Ovate Blade Adena Early Woodland Catalog #: P588 Uploaded by Carson Wright Suggested Data Citation: Thompson, Christine, Erin Powers, Carson Wright, and Kevin C. Nolan, 2021. FRHS_FR053, 3D Model .ply file. Digital Exhibit of Fort Recovery Historical Society's Precontact Collection, Fort Recovery Historical Society and Applied Anthropology Laboratories, Ball State University. Source: Objaverse 1.0 / Sketchfab
FR047, Middle Woodland, keeled gorget
Keeled Gorget Middle Woodland Catalog #: P616 Uploaded by Carson Wright Suggested Data Citation: Thompson, Christine, Erin Powers, Carson Wright, and Kevin C. Nolan, 2021. FRHS_FR047, 3D Model .ply file. Digital Exhibit of Fort Recovery Historical Society's Precontact Collection, Fort Recovery Historical Society and Applied Anthropology Laboratories, Ball State University. Source: Objaverse 1.0 / Sketchfab
Figure 2 from: Valatin G, Abildtrup J, Accastello C, Al-Tawaha A, Andreucci M, Atanasova S, Avdibegović M, Baksic N, Banasik K, Barquin J, Barstad J, Bastakova V, Becirovic D, Begueria S, Bethers U, Bihunova M, Blagojevic B, Bösch M, Bournaris T, Cao Y, Carvalho-Santos C, Chikalanov A, Cunha e Sá M, Czyżyk K, Daly H, Davies H, Del Campo A, Groot R, De Vreese R, Dostál T, El Mokaddem A, Finér L, Evans R, Fiquepron J, Frac M, Futter M, Garcia S, Gatto P, Geneletti D, Gezik V, Giupponi C, González-Sanchís M, Gordillo F, Gorriz E, Grigorova Y, Heinsoo K, Hochbichler E, Högbom L, Image M, Jacobsen J, Japelj A, Jelic S, Junk J, Juhasz C, Kagalou I, Kelly-Quinn M, Klamerus-Iwan A, Kluvankova T, Koeck R, Konovska I, Ostoic S, Krc J, Lavnyy V, Leonardi A, Libiete Z, Little D, Lo Porto A, Loukas A, Lyubenova M, Maric B, Martínez-López J, Martinez I, Maxim A, Metslaid M, Melvin A, Costică M, Mincev I, Morkvenas Z, Nevenic R, Nisbet T, O'hUallachain D, Olschewski R, Östberg J, Oszust K, Ovando P, Paletto A, Parpan T, Pettenella D, Malovrh Š, Planinšek Š, Podlipná R, Posavec S, Potočki K, Prokofieva I, Quinteiro P, Radocz L, Ristic R, Robert N, Rugani B, Sabanovic J, Sarvasova Z, Savoska S, Schleppi P, Schueler G, Shannon M, Silgram M, Srdjevic B, Stefan G, Stijovic A, Strange N, Tattari S, Teofilovski A, Termansen M, Thorsen B, Toth A, Trebs I, Tmušić N, Vasiliades L, Vedel S, Ventrubová K, Vuletic D, Winkel G, Yao R, Young S, Yousefpour R, Zahvoyska L, Zhang D, Zhou J, Žižková E (2017) PESFOR-W: Improving the design and environmental effectiveness of woodlands for water Payments for Ecosystem Services. Research Ideas and Outcomes 3: e13828. https://doi.org/10.3897/rio.3.e13828
Figure 2 - GANTT Diagram
Figure 1 from: Valatin G, Abildtrup J, Accastello C, Al-Tawaha A, Andreucci M, Atanasova S, Avdibegović M, Baksic N, Banasik K, Barquin J, Barstad J, Bastakova V, Becirovic D, Begueria S, Bethers U, Bihunova M, Blagojevic B, Bösch M, Bournaris T, Cao Y, Carvalho-Santos C, Chikalanov A, Cunha e Sá M, Czyżyk K, Daly H, Davies H, Del Campo A, Groot R, De Vreese R, Dostál T, El Mokaddem A, Finér L, Evans R, Fiquepron J, Frac M, Futter M, Garcia S, Gatto P, Geneletti D, Gezik V, Giupponi C, González-Sanchís M, Gordillo F, Gorriz E, Grigorova Y, Heinsoo K, Hochbichler E, Högbom L, Image M, Jacobsen J, Japelj A, Jelic S, Junk J, Juhasz C, Kagalou I, Kelly-Quinn M, Klamerus-Iwan A, Kluvankova T, Koeck R, Konovska I, Ostoic S, Krc J, Lavnyy V, Leonardi A, Libiete Z, Little D, Lo Porto A, Loukas A, Lyubenova M, Maric B, Martínez-López J, Martinez I, Maxim A, Metslaid M, Melvin A, Costică M, Mincev I, Morkvenas Z, Nevenic R, Nisbet T, O'hUallachain D, Olschewski R, Östberg J, Oszust K, Ovando P, Paletto A, Parpan T, Pettenella D, Malovrh Š, Planinšek Š, Podlipná R, Posavec S, Potočki K, Prokofieva I, Quinteiro P, Radocz L, Ristic R, Robert N, Rugani B, Sabanovic J, Sarvasova Z, Savoska S, Schleppi P, Schueler G, Shannon M, Silgram M, Srdjevic B, Stefan G, Stijovic A, Strange N, Tattari S, Teofilovski A, Termansen M, Thorsen B, Toth A, Trebs I, Tmušić N, Vasiliades L, Vedel S, Ventrubová K, Vuletic D, Winkel G, Yao R, Young S, Yousefpour R, Zahvoyska L, Zhang D, Zhou J, Žižková E (2017) PESFOR-W: Improving the design and environmental effectiveness of woodlands for water Payments for Ecosystem Services. Research Ideas and Outcomes 3: e13828. https://doi.org/10.3897/rio.3.e13828
Figure 1 - Types of PES Schemes
Figure 3 from: Valatin G, Abildtrup J, Accastello C, Al-Tawaha A, Andreucci M, Atanasova S, Avdibegović M, Baksic N, Banasik K, Barquin J, Barstad J, Bastakova V, Becirovic D, Begueria S, Bethers U, Bihunova M, Blagojevic B, Bösch M, Bournaris T, Cao Y, Carvalho-Santos C, Chikalanov A, Cunha e Sá M, Czyżyk K, Daly H, Davies H, Del Campo A, Groot R, De Vreese R, Dostál T, El Mokaddem A, Finér L, Evans R, Fiquepron J, Frac M, Futter M, Garcia S, Gatto P, Geneletti D, Gezik V, Giupponi C, González-Sanchís M, Gordillo F, Gorriz E, Grigorova Y, Heinsoo K, Hochbichler E, Högbom L, Image M, Jacobsen J, Japelj A, Jelic S, Junk J, Juhasz C, Kagalou I, Kelly-Quinn M, Klamerus-Iwan A, Kluvankova T, Koeck R, Konovska I, Ostoic S, Krc J, Lavnyy V, Leonardi A, Libiete Z, Little D, Lo Porto A, Loukas A, Lyubenova M, Maric B, Martínez-López J, Martinez I, Maxim A, Metslaid M, Melvin A, Costică M, Mincev I, Morkvenas Z, Nevenic R, Nisbet T, O'hUallachain D, Olschewski R, Östberg J, Oszust K, Ovando P, Paletto A, Parpan T, Pettenella D, Malovrh Š, Planinšek Š, Podlipná R, Posavec S, Potočki K, Prokofieva I, Quinteiro P, Radocz L, Ristic R, Robert N, Rugani B, Sabanovic J, Sarvasova Z, Savoska S, Schleppi P, Schueler G, Shannon M, Silgram M, Srdjevic B, Stefan G, Stijovic A, Strange N, Tattari S, Teofilovski A, Termansen M, Thorsen B, Toth A, Trebs I, Tmušić N, Vasiliades L, Vedel S, Ventrubová K, Vuletic D, Winkel G, Yao R, Young S, Yousefpour R, Zahvoyska L, Zhang D, Zhou J, Žižková E (2017) PESFOR-W: Improving the design and environmental effectiveness of woodlands for water Payments for Ecosystem Services. Research Ideas and Outcomes 3: e13828. https://doi.org/10.3897/rio.3.e13828
Figure 3 - PERT Diagram
Figure 2 from: Veranso-Libalah MC, Stone RD, Kadereit G (2017) Argyrella richardsiae, a new species of Melastomataceae from the wet miombo woodlands of south-central Africa. PhytoKeys 82: 113-121. https://doi.org/10.3897/phytokeys.82.12914
Figure 2 - Distribution map of Argyrella species.
Data for "Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought"
<p>This dataset contains data described in '<em>Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought</em>' (<a href="doi.org/10.1038/s43247-024-01448-x">publication link</a>). These data are a subset of those collected across the 50,000 ha region of interest (designation: GIL) across and beyond Gilé National Park, Mozambique. In particular, these data concern a ~350 ha subsection (designation: GIL04). See the Materials and Methods section of the paper for a description of how these data were collected. Additionally, data used for creating the graphs and charts of the manuscript are included. This dataset comprises:</p> <ul> <li><strong>Conventional inventory measurements:</strong> <ul> <li>Digitised forest inventory of plot GIL04-01, including automatic correction of taxonomic information, and attribution of basic wood density (.csv)</li> </ul> </li> <li><strong>Terrestrial laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL04-01_4587*_81616*.laz: 4 x 10 m tiled TLS point clouds</li> <li>0007MZ_GIL04-01_t974.laz: point cloud of an individual tree segmented from the above tiles</li> <li>0007MZ_GIL04-01_t974.mat: quantitative structural model constructed from the above individual tree point cloud</li> <li>0007MZ_GIL04-01_L4_AGB_10m.tiff: gridded estimate (10 m resolution) of TLS-derived aboveground biomass across GIL04-01</li> </ul> </li> <li><strong>Unoccupied aerial vehicle laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL04_4587*_81616*.laz: 4 x 50 m tiled UAV-LS point clouds overlapping the above TLS point clouds</li> <li>0007MZ_GIL04_L3*.tiff: UAV-LS-derived metrics of forest structure across GIL04 (10 m resolution unless stated otherwise)</li> <li>CHM: canopy height and digital elevation model (1 m resolution)</li> <li>RH: 100 relative height metrics in 1% intervals</li> <li>structure_metrics: 14 structural metrics (see paper for details)</li> <li>voxelvolumes: 3D voxel occupancy rates in 1 m bands from 0 to 50 m above terrain</li> <li>0007MZ_GIL04_L4_AGB.tiff: gridded estimate (10 m resolution) of predicted above-ground biomass across GIL04 (10 m resolution)</li> </ul> </li> <li><strong>Aerial laser scanning measurements and derived products:</strong> <ul> <li>0007MZ_GIL_458700_8161600.laz: 1 x 100m tiled ALS point cloud coincident with above UAV-LS point clouds</li> <li>0007MZ_GIL_L3*_clipped.tif: ALS-derived metrics of forest structure across GIL04, in the same format as above UAV-LS metrics</li> <li>0007MZ_GIL_L4_AGB_clipped.tif: gridded estimate (10 m resolution) of predicted above-ground biomass across GIL04 (10 m resolution)</li> </ul> </li> <li><strong>Data for manuscript graphs:</strong> <ul> <li>.csv files containing the data to reproduce Fig.3-5</li> </ul> </li> </ul>
FR040, Middle Woodland, Robbins phase point
Robbins Phase Points Early Woodland Catalog #: P609 Uploaded by Carson Wright Suggested Data Citation: Thompson, Christine, Erin Powers, Carson Wright, and Kevin C. Nolan, 2021. FRHS_FR040, 3D Model .ply file. Digital Exhibit of Fort Recovery Historical Society's Precontact Collection, Fort Recovery Historical Society and Applied Anthropology Laboratories, Ball State University. Source: Objaverse 1.0 / Sketchfab
FR043, Late Woodland, lowe-flared base
Lowe-Flared Base Late Woodland Catalog #: FR 3/144 P673 Uploaded by Carson Wright Suggested Data Citation: Thompson, Christine, Erin Powers, Carson Wright, and Kevin C. Nolan, 2021. FRHS_FR043, 3D Model .ply file. Digital Exhibit of Fort Recovery Historical Society's Precontact Collection, Fort Recovery Historical Society and Applied Anthropology Laboratories, Ball State University. Source: Objaverse 1.0 / Sketchfab
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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.