Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
59
datasets available to search
ShareScore release 0.7.1
Dataset results
59 results for “recent forests”
LANDIS-II PnET Simulation of Recent Trends in Forest Change in New England 2010-2060
The future forests of eastern North America will be shaped by at least three broad drivers: (i) vegetation change and natural disturbance patterns associated with the protracted recovery following colonial era land use, (ii) a changing climate, and (iii) a land-use regime that consists of geographically variable rates and intensities of forest harvesting, clearing for development, and land protection. We evaluated the aggregate and relative importance of these factors for the future forests of New England, USA by simulating a continuation of the recent trends in these drivers for fifty-years, nominally spanning 2010 to 2060. The models explicitly incorporate the modern distribution of tree species and the geographical variation in climate and land-use change. Using a cellular land-cover change model in combination with a physiologically-based forest landscape model, we conducted a factorial simulation experiment to assess changes in aboveground carbon (AGC) and forest composition. In the control scenario that simulates a hypothetical absence of any future land use or future climate change, the simulated landscape experienced large increases in average AGC—an increase of 53% from 2010 to 2060 (from 4.2 to 6.3 kg m-2). By 2060, climate change increased AGC stores by 8% relative to the control while the land-use regime reduced AGC by 16%. Among land uses, timber harvesting had a larger effect on AGC storage and changes in tree composition than did forest conversion to non-forest uses, with the most pronounced impacts observed on private corporate-owned land in northern New England. Our results demonstrate a large difference between the landscape’s potential to store carbon and the landscape’s current trajectory, assuming a continuation of the modern land-use regime. They also reveal aspects of the land-use regime that will have a disproportionate impact on the ability of the landscape to store carbon in the future, such as harvest regimes on corporate-owned lands. This
Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance
Disturbances can change the structural diversity of forests through time, which can be measured from three-dimensional data provided by LiDAR. Discrete-return LiDAR was used to measure a suite of 19 structural diversity metrics that describe the height, cover and openness, vegetation density, and internal and external heterogeneity of forest vegetation at NEON base plots. Discrete-return LiDAR point clouds from the NEON Aerial Observation Platform (DP1.30003.001) were downloaded September of 2020 and used to estimate the metrics within 40 x 40 m base plots. Metrics were estimated from base plots at 15 NEON forested sites from provisional LiDAR data available from 2014 to 2020. The workflow that produced the data was developed in the program R.
Data and modeling results for publication: Landscape genetics indicate recently increased habitat fragmentation in African forest-associated chafers
<ul> <li>DNA sequences: <em>cox1</em> and ITS1 alignments</li> <li>spatial records (in hypervolume archive)</li> <li>spatial principal component 1-3 used for <em>hypervolume</em> models (in hypervolume archive)</li> <li>Present and past species distribution models (SDMs): <ul> <li><em>biomod2</em> ensemble SDMs <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>biomod2</em> SDMs for single PMIP3 models <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>hypervolume</em> SDMs</li> </ul> </li> <li>landscape connectivity models <ul> <li>circuitscape (for F0, F1, and F2)</li> <li>least cost corridors and paths (for F0, F1, and F2)</li> </ul> </li> </ul>
Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest
<p>This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed. </p> <p>Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.</p>
Figure 4. A in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 4. A. Holotype adult Hyposmocoma desilvai sp. n., DR21D16E.1, DNA00252, reared from a "cigar" type case from Kamehamenui FR. B. Female genitalia of same specimen, slide KAA0902. C. Holotype adult H. makaohuna, DR21D16A.E3, DNA00255, reared from a "candywrapper" type case from Kamehamenui FR. D. Fe- male genitalia of same specimen, slide KAA0901. Abbreviations: pa = papilla anales, IX = segment IX.
Figure 3. A in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 3. A. Paratype adult of Hyposmocoma kukilakila sp. n., DR21D16B.E1, DNA00228, reared from a "tiny burrito" type case from Kamehamenui FR. B. Female genitalia of same specimen, slide KAA0899. C. Male genitalia, tegumen, ventral view, paratype slide KAA0960. D. Male genitalia, valvae and aedeagus, slide KAA0960, digitally dissected partial valvae with spurs intact below, paratype slide KAA0959. E. Pleural lobes on sternite VIII, slide KAA0959. F. Sternite sclerotization and protruding sternite hook on segment VII, slide KAA0959. Abbreviations: lb = left brachium, rb = right brachium, st = sterigma, sp = spur, la = left anellus lobe, ph = phallus, ra = right anellus lobe, ss = sternite sclerotization, rsh = right sternite hook.
Figure 6 in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 6. Larval cases of the new species, all to same scale. A. Hyposmocoma makaohuna, rearing lot DR21D16A, B. H. kamehamenui, rearing lot DR21D16B, C. H. desilvai, larval case of holotype (note case openings on both ends), D. H. starrorum, rearing lot DR21E1A (single case opening), E. H. kukilakila, rearing lot DR21D16B.
Figure 2 in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 2. Hyposmocoma starrorum sp. n. A. Holotype adult male DR21E1A.E8, DNA00319, reared from a "burrito" type case from Kamehamenui FR. B. Female genitalia, slide KAA0898. C. Male holotype genitalia, valvae and tegumen (right bra- chium folded over right valva), slide KAA0897. D. Phallus and ductus ejaculatorius, slide KAA0897. E. Sternite hooks on segment VII, slide KAA0897. F. Pleural lobes on sternite VIII. Abbreviations: sp = spur, la = left anellus lobe, ra = right anellus lobe, lb = left brachium, rb = right brachium, cp = costal process, o = ostium, st = sterigma, lsh = left sternite hook, rsh = right sternite hook.
Figure 1 in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 1. Map of East Maui with the 3,433 acres of Kamehamenui Forest Reserve indicated in red. Map tiles by Stamen Design, under CC BY 3.0. Data by Open- StreetMap, under ODbL.
Figure 5. A in Five New Species of Hawaiian Endemic Fancy Case Caterpillars from a Recently Established Forest Reserve on Maui (Cosmopterigidae: Hyposmocoma)
Figure 5. A. Holotype adult of H. kamehamenui, DR21D16B.E2, DNA00249, reared from a "burrito" type case from Kamehamenui FR. B. Male genitalia of same speci- men, tegumen and brachia, lateral view, slide KAA0900. C. Phallus, same specimen. D. Valvae, same specimen. E. Pleural lobes, same specimen. Abbreviations: rb = right brachium, t = tegumen, sp = spur, ph = phallus.
Data and code for: Rocky Mountain subalpine forests now burning more than any time in recent millennia
Open the record for dataset details and reuse information.
Recent fire history enhances semi-arid conifer forest drought resistance
Open the record for dataset details and reuse information.
Recent tree mortality dampens semi-arid forest die-off during subsequent drought
Open the record for dataset details and reuse information.
Recent tree diversity increase in NE Iberian forests following intense management release: a task for animal-dispersed and drought tolerant species
<ol> <li>Under increasing human-related threats to forests, many studies suggest that increasing tree species diversity may boost forest resilience by enhancing the range of species' responses to disturbances. However, it remains unclear whether passive or active forest management strategies should be applied to increase tree diversity. This issue would benefit from investigating which management and environmental factors, together with species' functional traits influence temporal changes in tree species diversity.</li> <li>We explored the influence of the bioclimatic region, land-use history, forest cover, protection, management, forest structure and changes in temperature and precipitation, to explain tree species diversity changes in NE Iberian forests, by comparing 3141 plots from the Spanish National Forest Inventory sampled between 1989 and 2016. Moreover, we assessed which species' functional traits (dispersal habit, drought and shade tolerance) were most relevant for diversity changes.</li> <li>After 27 years, tree species richness and diversity moderately increased in the tree and regeneration layers. This trend occurred mostly in long-established, non-recently managed forests and in those with a lower initial basal area. Increasing temperature had negative effects for diversity increase in the tree layer but positive for the regeneration compartment, while decreasing precipitation showed the opposite effects.</li> <li>Tree species with higher drought tolerance, and especially those animal-dispersed ones arriving from the regional pool, mostly contributed to the local diversity increase. This pattern occurred in all forest types, although the taxonomic array of species varied.</li> <li> <em>Synthesis and applications.</em> The main drivers influencing the passive increase in tree species diversity suggest a primary role of diminishing forest exploitation in this recovery process, fine-tuned by climatic changes. This ecological scenario has particularly favored animal-dispersed tree species with higher drought tolerance, which mostly led the diversity increase. A higher presence of such highly mobile and drought-tolerant species can be crucial to increase functional diversity and, ultimately, increase forest resilience under future scenarios of greater aridity. In light of these results, management strategies should continue fostering the restoration of diversity in once intensively exploited forests while ensuring the maintenance of the already gained tree species diversity.</li> </ol>
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
Recent photosynthates are the primary carbon source for soil microbial respiration in subtropical forests
<p class="Heading-Main"><span>Tropical and subtropical forests represent the largest terrestrial carbon pool. Elucidating the carbon sources for soil microbial respiration (Rm) in tropical and subtropical forests is of fundamental importance to the global carbon cycle in a warming world. Based on hourly measurements, we quantified Rm of <em>in situ </em>forest soil and soil cores from a subtropical forest. We found recent photosynthates, not soil organic carbon (SOC), contributed 88% ± 12% of the carbon source fueling Rm. The control of recent photosynthates on Rm is also supported by the close relationship between Rm and photosynthetically active radiation as well as literature data synthesis results. These results challenge conventional models based on the tenet that Rm is mainly regulated by soil temperature in all forest ecosystems. The results imply that the widely observed warming-induced Rm increases are largely explained by the enhanced input of recent photosynthates in tropical forests, not SOC consumption.</span></p>
Large contribution of recent photosynthate to soil respiration in tropical dipterocarp forest revealed by girdling
<b>Description: </b><p>The research site is one of the existing intensive carbon plots (Tower Plot) at the SAFE Project Experimental area. The area where the plot is located will be converted into oil palm plantation during 2015-2017 (for commercial purposes, not for research). The overarching aim of the project is to assess how the termination of the transport of sugars and defoliation alter forest ecosystem functioning and structure.The aim of the project is:<br>1. To quantify the contribution of photosynthate supply to soil respiration: via the contribution of roots and soil microbial communities utilising root-derived carbon.<br>2. To assess whether there is a relationship between root respiration and tree species.<br>To address these aims, we girdled trees in one half of the plot (0.5 ha), leaving the other half (0.5 ha) as a control. In girdling, a strip of bark (including cambium and phloem) was removed from around the trunk, with the aim of stopping the transport of sugars from the foliage into the roots and soil. The transport of sugars stop immediately, allowing us to quantify their role in the root and soil processes. The girdled trees will gradually defoliate and die due to the carbon starvation of the roots. We wish to emphasise that these trees would have been felled anyway during the conversion to oil palm - this project is not causing any additional deforestation.<br>The processes measured are:<br>- CO2 fluxes from soil measured with portable chambers from which a gas sample is drawn and analysed in the field with a portable instrument (CO2) <br>- Changes in tree circumference monitored with automatic dendrometer bands.<br>- Terrestrial laser scanning (T-lidar), non-destructive method to quantify the 3D structure of the forest stand.Pre-girdling data of all processes will be collected, starting at least two months before the girdling. The girdling took place in early 2016, and the monitoring continued for twelve months afterwards.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/28"><b>Tree girdling - BALI project</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC, the Ministry of Education, Youth and Sports of the Czech Republic (Grant, NE/K01627X/1, NE/G018278/1, INTER-TRANSFER LTT19018)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.4 (3))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5519572">here</a></p><p><b>Files: </b>This consists of 1 file: BALI_Nottingham_Girdling_Data_2021_rev.xlsx</p><p><b>BALI_Nottingham_Girdling_Data_2021_rev.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>CO2 and H2O data</b> (described in worksheet CO2_H2O_data)</p><p>Description: Tree identity and mortality collected taken January 2016- January 2017; Soil respiration, soil temperature and January moisture measurements taken January- March 2016 in a girdled tropical forest using a LiCor 8100a </p><p>Number of fields: 12</p><p>Number of data rows: 12548</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>daynight</b>: defined by 6pm to 6am (Field type: categorical)</li><li><b>date</b>: date of measurement (Field type: date)</li><li><b>plot</b>: subplot' in manuscript (Field type: id)</li><li><b>Rday</b>: relative data to the start of girdling (girdling day = 0) (Field type: id)</li><li><b>CO2</b>: soil CO2 efflux (Field type: numeric)</li><li><b>H2O</b>: soil volumetric moisture (Field type: numeric)</li><li><b>T</b>: soil temperature (Field type: numeric)</li><li><b>port</b>: refers to soil collar location (we allocated chamber port to soil collar location) (Field type: id)</li><li><b>portplot</b>: soil collar location nested within plot (Field type: id)</li><li><b>time</b>: time of measurement (24h) (Field type: numeric)</li><li><b>phase</b>: measurement period (see manuscript for phase definitions) (Field type: categorical)</li></ul></li><li><p><b>Tree mortality data</b> (described in worksheet Mortality_data)</p><p>Description: Tree census of trees surroudings the points where Licor 8100a measurements were taken</p><p>Number of fields: 20</p><p>Number of data rows: 259</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>ForestPlotsCode</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: id)</li><li><b>Subplot</b>: subplots 1-12 included in the manuscript (Field type: id)</li><li><b>CensusDate</b>: date when trees were originally measured (Field type: date)</li><li><b>TagNumber</b>: tree tag identity (Field type: id)</li><li><b>Height_m</b>: tree height (Field type: numeric)</li><li><b>Comments</b>: comments about the tree (Field type: comments)</li><li><b>Family</b>: tree family (Field type: taxa)</li><li><b>Genus</b>: tree genus (Field type: taxa)</li><li><b>SpeciesName</b>: tree species (Field type: comments)</li><li><b>WoodDensity</b>: wood density (Field type: numeric)</li><li><b>CrownProjection_Area_m2_in2016</b>: Crown Projection Area in 2016 (Field type: numeric)</li><li><b>X_m</b>: coordinates (Latitude) (Field type: numeric)</li><li><b>Y_m</b>: coordinates (Longitude) (Field type: numeric)</li><li><b>GirdlingDeathDate</b>: girdling tree death date (Field type: date)</li><li><b>Biomass_kgPerStem</b>: Biomass_kgPerStem (Field type: numeric)</li><li><b>Carbon_kgCperStem</b>: Carbon_kgCperStem (Field type: numeric)</li><li><b>mortality</b>: mortality (Field type: categorical)</li><li><b>DBHgrowth_cm_year</b>: DBHgrowth_cm_year (Field type: numeric)</li><li><b>DBHAnnualGrowthRate</b>: DBHAnnualGrowthRate (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-08-04 to 2017-02-07</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Plantae <br> -  -  Tracheophyta <br> -  -  -  Magnoliopsida <br> -  -  -  -  Lamiales <br> -  -  -  -  -  Lamiaceae <br> -  -  -  -  -  -  <i>Callicarpa</i> <br> -  -  -  -  Rosales <br> -  -  -  -  -  Urticaceae <br> -  -  -  -  -  -  <i>Pipturus</i> <br> -  -  -  -  -  -  <i>Dendrocnide</i> <br> -  -  -  -  -  -  <i>Oreocnide</i> <br> -  -  -  -  -  Moraceae <br> -  -  -  -  -  -  <i>Ficus</i> <br> -  -  -  -  Malpighiales <br> -  -  -  -  -  Achariaceae <br> -  -  -  -  -  -  <i>Hydnocarpus</i> <br> -  -  -  -  -  Euphorbiaceae <br> -  -  -  -  -  -  <i>Macaranga</i> <br> -  -  -  -  -  -  <i>Cephalomappa</i> <br> -  -  -  -  -  -  <i>Mallotus</i> <br> -  -  -  -  -  Phyllanthaceae <br> -  -  -  -  -  -  <i>Aporosa</i> <br> -  -  -  -  -  Ixonanthaceae <br> -  -  -  -  -  -  <i>Ixonanthes</i> <br> -  -  -  -  -  Calophyllaceae <br> -  -  -  -  -  -  <i>Calophyllum</i> <br> -  -  -  -  -  Violaceae <br> -  -  -  -  -  -  <i>Rinorea</i> <br> -  -  -  -  Ericales <br> -  -  -  -  -  Pentaphylacaceae <br> -  -  -  -  -  -  <i>Adinandra</i> <br> -  -  -  -  -  Sapotaceae <br> -  -  -  -  -  -  <i>Palaquium</i> <br> -  -  -  -  -  Symplocaceae <br> -  -  -  -  -  -  <i>Symplocos</i> <br> -  -  -  -  -  Ebenaceae <br> -  -  -  -  -  -  <i>Diospyros</i> <br> -  -  -  -  Malvales <br> -  -  -  -  -  Dipterocarpaceae <br> -  -  -  -  -  -  <i>Shorea</i> <br> -  -  -  -  -  -  <i>Dipterocarpus</i> <br> -  -  -  -  -  -  <i>Dryobalanops</i> <br> -  -  -  -  -  -  <i>Parashorea</i> <br> -  -  -  -  -  Malvaceae <br> -  -  -  -  -  -  <i>Pterospermum</i> <br> -  -  -  -  -  -  <i>Scaphium</i> <br> -  -  -  -  -  -  <i>Brownlowia</i> <br> -  -  -  -  -  -  <i>Sterculia</i> <br> -  -  -  -  -  -  <i>Microcos</i> <br> -  -  -  -  -  -  <i>Neesia</i> <br> -  -  -  -  -  -  <i>Diplodiscus</i> <br> -  -  -  -  Laurales <br> -  -  -  -  -  Lauraceae <br> -  -  -  -  -  -  <i>Actinodaphne</i> <br> -  -  -  -  -  -  <i>Litsea</i> <br> -  -  -  -  -  -  <i>Eusideroxylon</i> <br> -  -  -  -  Celastrales <br> -  -  -  -  -  Celastraceae <br> -  -  -  -  -  -  <i>Lophopetalum</i> <br> -  -  -  -  Magnoliales <br> -  -  -  -  -  Myristicaceae <br> -  -  -  -  -  -  <i>Knema</i> <br> -  -  -  -  -  Annonaceae <br> -  -  -  -  -  -  <i>Goniothalamus</i> <br> -  -  -  -  -  -  <i>Polyalthia</i> <br> -  -  -  -  -  -  <i>Maasia</i> <br> -  -  -  -  Vitales <br> -  -  -  -  -  Vitaceae <br> -  -  -  -  -  -  <i>Leea</i> <br> -  -  -  -  Myrtales <br> -  -  -  -  -  Myrtaceae <br> -  -  -  -  -  -  <i>Syzygium</i> <br> -  -  -  -  -  Lythraceae <br> -  -  -  -  -  -  <i>Duabanga</i> <br> -  -  -  -  Cornales <br> -  -  -  -  -  Cornaceae <br> -  -  -  -  -  -  <i>Alangium</i> <br> -  -  -  -  Gentianales <br> -  -  -  -  -  Rubiaceae <br> -  -  -  -  -  -  <i>Neolamarckia</i> <br> -  -  -  -  -  -  <i>Neonauclea</i> <br> -  -  -  -  -  -  <i>Urophyllum</i> <br> -  -  -  -  -  -  <i>Pleiocarpidia</i> <br> -  -  -  -  Fabales <br> -  -  -  -  -  Fabaceae <br> -  -  -  -  -  -  <i>Saraca</i> <br> -  -  -  -  -  Polygalaceae <br> -  -  -  -  -  -  <i>Xanthophyllum</i> <br> -  -  -  -  Cucurbitales <br> -  -  -  -  -  Tetramelaceae <br> -  -  -  -  -  -  <i>Octomeles</i> <br> -  -  -  -  Fagales <br> -  -  -  -  -  Fagaceae <br> -  -  -  -  -  -  <i>Lithocarpus</i> <br> -  -  -  -  -  -  <i>Castanopsis</i> <br> -  -  -  -  Sapindales <br> -  -  -  -  -  Sapindaceae <br> -  -  -  -  -  -  <i>Nephelium</i> <br> -  -  -  -  -  -  <i>Dimocarpus</i> <br> -  -  -  -  -  -  <i>Pometia</i> <br> -  -  -  -  -  Meliaceae <br> -  -  -  -  -  -  <i>Dysoxylum</i> <br> -  -  -  -  -  -  <i>Aglaia</i> <br> -  -  -  -  -  Burseraceae <br> -  -  -  -  -  -  <i>Canarium</i> <br> -  -  -  -  -  Anacardiaceae <br> -  -  -  -  -  -  <i>Buchanania</i> <br></div><p></p>
Recent photosynthates are the primary carbon source for soil microbial respiration in subtropical forests
Open the record for dataset details and reuse information.
Data from: Land-use legacies influence tree water-use efficiency and nitrogen dynamics in recently established European forests
Open the record for dataset details and reuse information.
Recent tree diversity increase in NE Iberian forests following intense management release: A task for animal-dispersed and drought tolerant species
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.