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2,618 results for “adjacency”
Groundwater levels and temperature in coastal tundra adjacent to Simpson Lagoon, Alaska, 2022
The coastal tundra adjacent to Simpson Lagoon, along Alaska's Beaufort Sea coast, was visited in July and September/October 2022 to assess coastal groundwater dynamics along the lagoon. Groundwater levels were monitored in five piezometers aligned in a transect oriented perpendicular to Simpson Lagoon, as well as surface water levels in the lagoon. Loggers were deployed on July 22, 2022, and collected on September 29, 2022. Data include surface water and groundwater elevation data from July 22, 2022, to September 29, 2022, with the elevation relative to a local datum. All levels have been corrected for barometric pressure (see methods). Also included is groundwater temperature data from the well closest to the lagoon (Well 5).
Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset
<p><span>The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022.<span> </span>Data were generated<span> </span>as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset. <br></span></p> <p><span>Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., <strong>Jaspers, C.</strong> (submitted) Biod</span><span>iversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. <em>Data in Brief. </em></span></p>
Schematic 3D reconstruction hypothesis of the house of the painter Gillis van Coninxloo at the Oude Turfmarkt and adjacent houses
<p>This is a schematic, grey scale 3D reconstruction of the vanished house of the painter Gillis van Coninxloo and adjacent houses resulting from the research conducted in the framework of the <em>Virtual Interiors</em> project. The research questions that this 3D reconstruction aimed to explore relate to the identification of the exact location of the house on the Oude Turfmarkt and its internal spatial arrangement. Especially the references that are contained in Coninxloo's probate inventory to a ‘Coninxloos winckel’ and an ‘achter winckel’ on the first floor of his house were investigated with the 3D model. </p> <p>An introduction to the Coninxloo case study and to the first phase of the 3D reconstruction project of his house is briefly presented in C. Piccoli and W. Li 2021. ‘Dealing with multidimensional uncertainty: The house of the painter Gillis van Coninxloo’, https://www.virtualinteriorsproject.nl/2021/08/19/dealing-with-multidimensional-uncertainty-the-house-of-the-painter-gillis-van-coninxloo/ (last accessed November 2022). An update on archival research and new insights on this and the neighbouring houses is given in C. Piccoli 2022. ‘The house of Gillis van Coninxloo at the Oude Turfmarkt: New insights’ https://www.virtualinteriorsproject.nl/2022/11/23/the-house-of-gillis-van-coninxloo-at-the-oude-turfmarkt-new-insights/ (last accessed November 2022).</p> <p>The sources that were used to propose this reconstruction hypothesis are listed in the *.csv file.</p> <p>Note: This 3D reconstruction is a provisional version and must be considered hypothetical. Aspects that could be clarified by further research include a possible difference in ground floor’s level between the front and the back in Coninxloo’s house, which would impact the spatial arrangement of the interior and require the presence of steps to bridge the two parts.</p> <p><strong>Historical and archival research</strong>: Chiara Piccoli, Bart Reuvekamp, Frans Grijzenhout.<br> <strong>3D modelling</strong>: Chiara Piccoli<br> <strong>3D modelling software</strong>: Blender<br> <strong>Acknowledgements</strong>: Virtual Interiors project, Gabri van Tussenbroek, Weixuan Li, Judith Brouwer, Madelon Simons.</p>
Tree inventory for adjacent stands of Picea mariana and Betula neoalaskana located in the 1958 Murphy Dome fire scar - 2012
This dataset contains the diameter of all trees measured as part of a project examining tree species affects on forest stand characteristics and plant-soil-microbial feedbacks. Study was conducted across 3 spatially explicit blocks containing adjacent stands of Picea mariana and Betula neoalaskana in forest that established following the 1958 Murphy Dome fire. All included tree diameter measurements were collected in Summer 2012.
Digital Elevation Model (DEM) of the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia
Topographic and bathymetric data were collected for the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia, using high-precision multibeam SONAR equipment. The bathymetric survey was performed from 09-Dec-2009 to 12-Dec-2009. This study was conducted to create a base map of bathymetry, morphology and physical habitat of the Duplin River, which is a primary study site of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) project.
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Indor इंदौर (Guna District, Madhya Pradesh). Hero-stone dated VS 1177 with adjacent image of Hanumān.
<p>Indor इंदौर (Guna District, Madhya Pradesh). Hero-stone dated VS 1177 with adjacent image of Hanumān.</p>
Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography
<p>We provide: the tomographic model for dephts 68 to 768 km as text files, where the first column is the longitude, the second is the latitude and the third if the velocity perturbation; the proposed limits for the Paranapanema Block, Luiz Alves Craton and Rio Apa Craton as a csv file (Figure 12 of the paper), where the first column is the name of the feature, the second is the longitude and the third is the latitude; and the abstract for the paper "Lithospheric architecture of the Paranapanema Block and adjacent nuclei using multiple-frequency P-wave seismic tomography".</p>
Udayagiri, Madhya Pradesh. Cave 4 and adjacent shrine.
<p>Udayagiri, Madhya Pradesh. Cave 4 entrance and adjacent shrine dedicated to the mother goddesses, seen from the south. As documented in 11/2007.</p>
Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions
<p><strong>Introduction</strong></p> <p>Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the <strong>Pan-Tibetan Highlands </strong>(new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the <strong>Tibetan Plateau</strong> and three adjacent mountain regions: the <strong>Himalaya</strong>, <strong>Hengduan Mountains</strong> and <strong>Mountains of Central Asia</strong>, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.</p> <p>The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:</p> <p>(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.</p> <p>(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.</p> <p>(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.</p> <p>Note: <strong>High Mountain Asia</strong>: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. <strong>Third Pole</strong>: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ <em>sensu lato</em> defined by some previous studies, but is more specific.</p> <p>More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.</p> <p> </p> <p><strong>Data processing</strong></p> <p>We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the <em>Layer to KML</em> tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the <em>shapefile</em> function of the raster package, and wrote it as a geojson file using the <em>geojson_write</em> function in the geojsonio package.</p> <p> </p> <p><strong>Version</strong></p> <p>Version 2022.1.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.</p> <p> </p> <p><strong>Citation</strong></p> <p>Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision</p> <p> </p> <p>Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940</p> <p> </p> <p><strong>Contacts</strong></p> <p>Dr. Jie LIU: E-mail: <a>liujie@mail.kib.ac.cn</a>;</p> <p>Mr. Guangfu ZHU: <a>zhuguangfu@mail.kib.ac.cn</a></p> <p>Institution: Kunming Institute of Botany, Chinese Academy of Sciences</p> <p>Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China</p> <p> </p> <p><strong>Copyright</strong></p> <p>This dataset is available under the Attribution-ShareAlike 4.0 International (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>).</p>
A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA
<p>This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: <em>A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA</em> (Bonner et al., 2022).</p> <p>All snow observation and meteorological forcing data are available as both .nc and .mat files.<br> Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.</p> <p>This dataset will continue to be updated, via this repository, as additional years of data are collected.</p>
Biogeographic history of a large clade of ectomycorrhizal fungi, the Russulaceae, in the Neotropics and adjacent regions
<p>## Metadata</p> <p>backbone_accessions.tsv - GenBank/INSDC accession numbers for LSU, rpb1 and rpb2 accessions used for the Russulaceae backbone tree including 472 taxa.</p> <p>ITS_sequences_OTUs.tsv - Metadata for all 34,624 ITS sequences used in the study. Columns: "accession": accession ID in analysis – GenBank/INSDC or UNITE accession number for compiled data, lab ID for newly generated data; "specimen": specimen/voucher number, for newly generated sequences; "INSDC_accession": INSDC/GenBank accession for new newly generated data; "taxon": specimen identification; "New": whether ITS sequences was generated in this study (*); "OTU": name of cluster/OTU, if not the sequence accession itself (*); "In_tree": whether sequence is represented in the Russulaceae supertree after filtering steps (*), "lb" long-branch accession removed during tree estimation, "ol" outlier removed during tree estimation; "area": biogeographic area assigned.</p> <p> </p> <p>## Sequences and alignments</p> <p>backbone_concat.fasta - Concatenated LSU-rpb1-rpb2 alignment for 372 backbone taxa.</p> <p>backbone_concat_part.txt - Gene partitions and substitution models applied to the backbone alignment.</p> <p>einsi_clade1_Russula_trimmed.fasta - Alignment of 2,279 representative ITS sequences in the Russula clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade2_LactariusMultifurca_trimmed.fasta - Alignment of 621 representative ITS sequences in the Lactarius-Multifurca clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade3_Lactifluus_trimmed.fasta - Alignment of 482 representative ITS sequences in the Lactifluus clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p> </p> <p>## Phylogenetic trees</p> <p>12_make_supertree.R - R script for grafting clade trees onto the backbone tree to produce a supertree.</p> <p>backbone_calibrated.nwk - Time-calibrated Russulaceae backbone phylogeny.</p> <p>backbone_TBE.raxml.support - Russulaceae backbone phylogeny annotated with transfer bootstrap expectation support values.</p> <p>clade1_Russula_TBE.raxml.support - Russula subclade ITS phylogeny (2,279 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade2_LactariusMultifurca_TBE.raxml.support - Lactarius-Multifurca subclade ITS phylogeny (621 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade3_Lactifluus_TBE.raxml.support - Lactifluus subclade ITS phylogeny (482 tips), annotated with transfer bootstrap expectation support values.</p> <p>supertree_calibrated.nwk - Combined Russulaceae supertree, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade1_Russula.nwk - Russula subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade2_LactariusMultifurca.nwk - Lactarius-Multifurca subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade3_Lactifluus.nwk - Lactifluus subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p> </p> <p>## Biogeographic analysis</p> <p>3_disp_counts.R - R script to count dispersal events between biogeographic areas, based on stochastic mapping output.</p> <p>9_disp_count_time.R - R script to count dispersal events to and from each area through time, based on stochastic mapping output.</p> <p>area_codes.tab - Area letter coding and colours used for biogeographic analysis and plotting.</p> <p>area_shapes.zip - Shapefiles for the nine biogeographic areas defined, based on merged areas from Dinerstein et al. 2017 (https://doi.org/10.1093/biosci/bix014) and Löwenberg-Neto (2014: https://doi.org/10.11646/zootaxa.3802.2.12; 2015: https://doi.org/10.11646/10.11646/zootaxa.3985.4.9).</p> <p>areas_manually_zenodo.csv - Manual assignment of 800 ITS sequences to biogeographic areas based on associated literature records or metadata.</p> <p>corHMM_ER.Rdata - R data archive with input data and results for the corHMM/Mv biogeographic area reconstruction.<br> <br> corHMM_ER_stoch_maps.Rdata - R data archive with results from the corHMM/Mv biogeographic stochastic mapping.</p> <p>disp_counts_focal.tab - Dispersal counts to and from each focal area through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_counts_sam_afr.tab - Dispersal counts between Afrotopics and lowland tropical S. America through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_matrix_025.txt - Dispersal rates between biogeographic areas (2.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_975.txt - Dispersal rates between biogeographic areas (97.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_median.txt - Dispersal rates between biogeographic areas (median values), based on stochastic mapping output.</p> <p> </p> <p>## Diversification analysis</p> <p>5_rates_per_area.R - R script to partition diversification rates by biogeographic area, both overall and through time, based on BAMM diversification rates and area stochastic mapping.</p> <p>event_data.txt - Posterior samples of diversification rate regimes estimated with BAMM.</p> <p>div_rates_area_overall.txt - Overall diversification rates per biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_025.tsv - Diversification rates through time (2.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_975.tsv - Diversification rates through time (97.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_median.tsv - Diversification rates through time (means) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>mcmc_out.txt - BAMM posterior sample characteristics.</p>
Data underlying the article "Effect of Submergence on the Lateral Exchange between Groyne Fields and their adjacent Main Channel"
<p>The paper addresses the identification of the mechanisms that dominate the flow around a series of obstacles placed at the sidewall of a channel, representing fluvial groynes. Accordingly, 2D velocity fields were measured through Particle Image Velocimetry in a horizontal plane spanning the area between groynes and an adjacent portion of the main channel. Four experimental cases were performed, addressing two groyne separations and two submergence conditions (emerged and submerged). For the submerged case, the ratio water depth to groyne height was 1.3. Groyne separations were characterized according to the corresponding width-to-length ratio of the groyne field (lambda = W/L = 1 and 2). A complete description of the experimental conditions, objectives and outcomes can be found in the article.</p> <p>The following data is included:</p> <ol> <li>Meanfields_[case].csv: spanwise and streamwise components of the velocity field, velocity magnitude and uv component of the Reynolds stress tensor averaged over time.</li> <li>Reynoldsstressesprofiles_[case].csv: uv component of the Reynolds stress tensor averaged over time at selected transverse profiles.</li> <li>PSD_[case].csv: power spectral densities computed from fluctuating velocity series extracted at selected locations.</li> <li>Autoccorrelation_[case].csv: normalized transverse autocorrelation functions computed from fluctuating velocity series extracted at selected locations.</li> <li>PODenergycontribution.csv: energy contribution from the first 20 POD modes computed for the case studies.</li> <li>PODtemporalcoefficients.csv: temporal coefficients obtained from the first two modes for the case studies.</li> <li>PODspectra_[case].csv: spectra of the temporal coefficients corresponding to modes 1 to 4 for the cases in study.</li> <li>PODspatialmodes_[case].csv: first two spatial modes computed for the case studies.</li> </ol>
Biogeochemical observations in adjacent mesoscale eddies of opposite polarity
<p>Datasets used for the analyses reported in the manuscript titled "Biogeochemical dynamics in adjacent mesoscale eddies of opposite polarity".</p> <p>For all files, the suffix HL4 indicates the expedition HOE-LEGACY 4, while the suffix MESOSCOPE indicates the MESO-SCOPE expedition.</p> <p>Temperature profiles measured underway across adjacent eddies are saved in files UnderwayTemperature*.csv. Station coordinates are saved in UnderwayCoordinates*.csv</p> <p>Shipboard vertical profiles of dissolved oxygen, chlorophyll fluorescence, and potential density anomaly are saved in files TransectOxygen*.csv, TransectFluorescence*.csv, and TransectSigma*.csv, respectively. Station coordinates are saved in TransectCoordinates*.csv</p> <p>Inorganic nutrient concentrations measured across adjacent eddies are saved in files EddyNutrients*.csv.</p> <p>Particulate carbon, chlorophyll a, beam attenuation, and chlorophyll fluorescence in the eddy centers are saved in file ParticlePigmentComparison_15m.csv and ParticlePigmentComparison_DCM.csv for the depth of 15 m and the depth of the DCM, respectively.</p> <p>Cell counts from flow cytometry in the eddy centers are saved in files FlowCytometry_cyclone_*.csv and FlowCytometry_anticyclone_*.csv. This files also report the depth of the DCM for each eddy center.</p> <p>Imaging FlowCytobot (IFCb) measurements during MESO-SCOPE are reported in files IFCB_Class_DCM&15m_MESOSCOPE.csv for classes and IFCB_Genera_DCM_MESOSCOPE.xlsx for genera.</p>
Crustal thickness and Vp/Vs database from the Amazonian Craton and adjacent provinces
<p>This database includes results of crustal thickness and Vp/Vs estimated from stations located in the Amazonian Craton and adjacent provinces.</p> <p>If you used this database, please cite:<br> <br> ALBUQUERQUE, D. F. et al. Crustal structure of the Amazonian Craton and adjacent provinces in Brazil. Journal of South American Earth Sciences, v. 79, p. 431–442, 2017. DOI: 10.1016/j.jsames.2017.08.019</p> <p> </p>
Sri Ksetra, Bago, Myanmar. Burial urn as excavated in 1912 at the Pyutaik terrace adjacent to the Payama stūpa.
<p>Sri Ksetra, Bago, Myanmar. Burial urn as excavated in 1912 at the Pyutaik terrace adjacent to the Payama stūpa. </p>
Tree ring width data for trees adjacent to the BBC collapse scar
This data set contains ring width data for trees adjacent to the BBC collapse scar. We used dendrochronology to link paleoecological data with modern observations of the response of this system to fire. Tree ring analysis provides a record of the response of the black spruce trees to changing climate and ongoing thermokarst, allowing for speculation about the response of this landscape to future climate change. We harvested twenty-one fire-killed tree cross-sections from the margin of the collapse and in the surrounding burn in the growing season of 2004. We measured ring width (sliding stage, Velmex Inc., Bloomfield, NY, USA, resolution: 0.001mm) for two radial transects of the tree cross-sections. To remove the age-related variation in growth rate, we crossdated trees with Cofecha and standardized ring-widths with the program ARSTAN (Richard Holmes, Laboratory of Tree Ring Research, University of Arizona). We recorded the presence of compression-wood for each tree ring, an indicator of leaning which is interpreted to be related to frost-heaving and permafrost collapse (Camill and Clark, 1998). This data set includes temperature and precipitation data from a composite of climate data from the University Experiment Station (1906-1947) and Fairbanks International Airport (1948-2000) (Wilmking et al., 2004).
Fungal community structure associated with seedlings that established post-fire and adjacent resprouting shrubs at Finger Mountain and Nome Creek, Alaska
This dataset contains characterizations of fungal community structure associated with seedlings that established after fire and adjacent resprouting shrubs. It also includes variables that describe proximity to the closest resprouting shrub, fire severity, vegetation, and substrate associated with seedlings and adjacent resprouting shrubs harvested at Finger Mountain and Nome Creek in 2009.
ARISA profiles for root-associated fungal communities associated with seedlings that established after fire and adjacent shrubs harvested at Finger Mountain and Nome Creek in 2009
This dataset contains the ARISA profiles of the root-associated fungal communities associated with seedlings that established after the 2004 fires and the closest resprouting shrub.
Birds observed in newly hayed and adjacent reference salt marsh sites in Rowley, MA, August through September 2000 and August 2001, PIE LTER.
Birds observed in newly hayed marsh and adjacent reference marsh sites near Stackyard Rd. and Patmos Rd, Rowley, Massachusetts. August through September 2000 and August 2001.
ScienceDex guides
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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.