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348 results for “Core data”
caseysaenger/ForamMgCa_PSM: files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data".
<p>files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data". Saenger, C. and M. N. Evans. Resubmitted to Paleoceanography and Paleoclimatology, May 3, 2019.</p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </p>
Core log descriptions and sediment grain size data for Hurricane Ian sediment cores collected in Lee County, Florida, USA
<p>These data represent qualitative and quantitative measurements of sediment cores collected from various environments following the landfall of Hurricane Ian. These sediment cores were collected using pound coring techniques up to 2m into the subsurface to characterize the sedimentological signature of storm deposits resulting from Hurricane Ian. More details regarding these measurements and interpretations of storm deposits can be found in the folllowing manuscript:</p> <p>McCormick, W.M., Briggs, T.R., Hauptman, L.H., Wang, P., Morphologic and sedimentological signatures resulting from Hurricane Ian, southwest Florida, USA: Insight into intra-storm bidirectional sediment transport processes (In Review). </p>
Shrub inventory data such as shrub identity, height and biomass in a 20x5m core plot at Mt. Kilimanjaro
<p>This dataset describes position and sizes of all shrubs above 130 cm high in all plots, also fruiting and flowering events in KiLi project. -999999 represents NA in numeric variables. </p> <p>The shrub inventory was carried out within a 5 × 20 m subplot in the centre of each plot. Within this subplot, the shrub layer was defined as consisting of all woody stems exceeding 1.3 m in height, but below 10 cm dbh and thus not included in the tree inventory. We measured dbh at 1.3 m with a diameter tape (Forestry Suppliers; for dbh's above 3 cm) or a caliper (for dbh's below 3 cm) and the height of each shrub with a hypsometer.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning
<p>Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning</p> <p>See paper at: https://osf.io/preprints/socarxiv/hyc4p</p> <p>In the last two decades, tax avoidance has risen to the top of the agenda of policy makers and international organizations. The majority of political action and academic research has focused on the macro-level of states, pointing towards the responsibility of ‘tax havens’ or ‘offshore financial centers’. Research on the micro-level has demonstrated the importance of non-state actors who facilitate tax planning, but tax advisors have never been studied systematically with global data. In this paper, we connect the micro and macro levels. We map tax advisors geographically using a novel empirical approach based on LinkedIn. We show that tax advisors generally locate in large cities in the EU and OECD, rather than in places targeted as ‘tax havens’. We further consider what determines the locations of tax advisors. Using multiple regression analysis, we find that locations of tax advisors does not correlate with the location of corporate profits, financial secrecy, or economic activity. Rather, it correlates with the managerial and financial activity. Our results underscore the core-periphery structure in offshore finance. Effective regulation of tax avoidance should focus on tax advisors, not only on the destination of money flows, since the active facilitation does not occur in those places.</p>
Lake Cadagno sediment core hyperspectral imaging and pigment data tables
<p>Data Tables related to the manuscript "Hyperspectral imaging sediment core scanning tracks high-resolution Holocene variations in (an)oxygenic phototrophic communities at Lake Cadagno, Swiss Alps" in submission. </p>
Gulf of Mexico planktonic foraminifera and stable isotope data from core EN-032-18PC spanning MIS 9 to MIS 5
<p><em>Database of the accepted manuscript</em>: Arellano-Torres et al., 2023. The Loop Current circulation over the MIS 9 to MIS 5 based on planktonic foraminifera assemblages from the Gulf of Mexico. Paleoceanography and Paleoclimatology, DOI: 10.1029/2022PA004568</p> <p>In the sediment Core EN-032-18PC collected below the influence of the Loop Current in the eastern Gulf of Mexico, we studied mixed layer conditions and the intensity of the surface and subsurface waters flowing from the Caribbean to the gulf. This database includes analyses of 136 samples in three Supplementary Tables. (Table S1) Bulk sediment and sand fraction (>62 μm) weight (g), the absolute abundance (tests per sample) of 33 species of planktonic foraminifera. (Table S2) Relative abundance (%) of planktonic foraminifera and factor loadings of two factors (Q-mode factor analysis). (Table S3) Stable isotopes (δ<sup>18</sup>O-PDB and δ<sup>13</sup>C-PDB) (‰) of <em>Globigerinoides ruber</em> (white) and the loess-smoothing of the series with polynomial regression.</p>
Sea ice core biogeochemical data collected during the 2019 SCALE Winter Cruise
<p><strong>Title: </strong>Biogeochemical profiles of sea ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019.</p> <p> </p> <p><strong>Authors:</strong> Riesna R. Audh, Siobhan Johnson, Mark Hambrock, Hazel Little, Joshua Mirkin, Emmanuel Omatuku, Benjamin Hall, Tokoloho Rampai, Keith MacHutchon, Sebastian Skatulla, Sarah E. Fawcett, Marcello Vichi</p> <p> </p> <p><strong>Data Description:</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>Biogeochemical profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p> <p> </p> <p>A total of four sea ice cores (cores) were sampled during the cruise. Two cores were collected overboard on a consolidated floe that was accessed via a personnel carrier suspended by the ship’s forward crane. Two cores were collected from a pancake that was lifted aboard the ship via a net that was attached to the ship’s aft crane and placed on the helideck for sampling. Profiles were obtained by cutting the cores using a bandsaw in a cold laboratory at -10 °C. The cores were cut into approximately 0.05 m segments, starting from the bottom of the core. These segments were allowed to melt in the dark in an insulated box. The meltwater was filtered for chlorophyll measurements (Welschmeyer, 1994) and the filtrate was analysed for oxygen isotopes (Walker and others, 2015), ammonium (Holmes et al., 1999), phosphate, nitrate, nitrite and silicate (using a SEAL AA500 segmented flow autoanalyser). These values are reported at the depth of the top of the segment in the core in μM. In order to facilitate comparison with the seawater concentrations below the ice, the in-ice nutrients (including NH4+) were salinity normalised using the equation of Fripiat and others (2017):</p> <p> </p> <p><em>C</em><em>norm</em><em> = C</em>SwS<em> </em><em> </em></p> <p> </p> <p>Where C is the measured bulk concentration, Sw is the salinity of the seawater, and S is the corresponding measured bulk salinity of the ice segment.</p> <p> </p> <p>Although sampling of the core occurred from the bottom of the core to the top of the core, the data are reported as the top of the core (snow/ice interface) being 0 m (depth=0 m). </p> <p> </p> <p><strong>This research has been funded by the National Research Foundation of South Africa (NRF)</strong></p> <p><br> </p> <p><strong>Cruise:</strong> VOY-038 (SCALE2019-WINTER) (URL: https://scale.org.za/)</p> <p><strong>Station(s):</strong> VOY-038-MIZ3A</p> <p>VOY-038-MIZ1D</p> <p><strong>Position(s):</strong> -58.13783 S; 0.00442 W</p> <p>-56.8017 S; 0.30262 E</p> <p><strong>Date/Time:</strong> 2019-07-27/10:38:00</p> <p>2019-07-28/09:15:00</p> <p><strong>Method(s):</strong> Overboard coring</p> <p>Pancake lifting via aft crane, on deck coring</p> <p><strong>Parameters:</strong><strong> </strong>Station Number (Station)</p> <p>Date/Time of station (Date/Time)</p> <p>Latitude of station (Latitude)</p> <p>Longitude of station (Longitude)</p> <p>Ice type (Ice Type)</p> <p>Core ID(Core), Pancake identifier A/B/C/D</p> <p>Oxygen isotopes (d18O)</p> <p>Chlorophyll (Chl-a)</p> <p>Ammonium (NH4)</p> <p>Nitrate + Nitrite (NO3+NO2)</p> <p>Nitrite (NO2)</p> <p>Phosphate (PO4)</p> <p>Silicate (Si)</p> <p>Nitrate (NO3)</p> <p>Salinity of the ice segment from physical cores (IceSalinity)</p> <p>Standard deviation of the salinity average from physical cores (IceSalinityStdev)</p> <p>Seawater salinity from CTD (SeawaterSalinity)</p> <p>Salinity normalised nitrate+nitrite (N03+N02_Avg_SalinityNormalised)</p> <p>Salinity normalised ammonium (NH4_SalinityNormalised)</p> <p>Salinity normalised nitrite (NO2_SalinityNormalised)</p> <p>Salinity normalised phosphate (PO4_SalinityNormalised)</p> <p>Salinity normalised silicate (Si_SalinityNormalised)</p> <p>Salinity normalised nitrate (NO3_SalinityNormalised)</p> <p><br> </p> <p> </p> <p><strong>Keywords: </strong>sea ice cores, Antarctica, pancake ice, sea ice, biogeochemistry, winter</p> <p> </p> <p><strong>References</strong><strong>:</strong></p> <p> </p> <p>Fripiat, F., Meiners, K.M., Vancoppenolle, M., Papadimitriou, S., Thomas, D.N., Ackley, S.F., Arrigo, K.R., Carnat, G., Cozzi, S., Delille, B. and Dieckmann, G.S., 2017. Macro-nutrient concentrations in Antarctic pack ice: Overall patterns and overlooked processes. Elementa: Science of the Anthropocene, 5. </p> <p> </p> <p>Holmes, R.M., Aminot, A., Kérouel, R., Hooker, B.A. and Peterson, B.J., 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences, 56(10), pp.1801-1808. </p> <p> </p> <p>Walker, S.A., Azetsu‐Scott, K., Normandeau, C., Kelley, D.E., Friedrich, R., Newton, R., Schlosser, P., McKay, J.L., Abdi, W., Kerrigan, E. and Craig, S.E., 2016. Oxygen isotope measurements of seawater (18O/16O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS). Limnology and Oceanography: Methods, 14(1), pp.31-38. </p> <p> </p> <p>Welschmeyer, N., 1994. A method for the determination of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, pp.1985-1992. </p> <p> </p>
Net primary production (NPP) and climate data from Sevilleta LTER core and control sites in desert grassland and shrubland ecosystems, 1999 - 2017
This dataset and R code were used to create the figures, tables and statistical analyses for the following publication: Rudgers, JA et al. 2018. Climate sensitivity functions and net primary production: A framework for incorporating climate mean and variability. Ecology. Data were collected by the Sevilleta LTER program, which is located in the Sevilleta National Wildlife Refuge (SNWR), New Mexico. These are long-term, continuing data sets. Data collection started in 1999 at the black grama grassland and creosote shrubland, and in 2002 for blue grama grassland. Meteorological stations started recording data as early as 1989. The study abstract from Rudgers et al. 2018 is: Understanding controls on net primary production (NPP) has been a long-standing goal in ecology. Climate is a well-known control on NPP, although the temporal differences among years within a site are often weaker than the spatial pattern of differences across sites. Climate sensitivity functions describe the relationship between an ecological response (e.g., NPP) and both the mean and variance of its climate driver (e.g., aridity index), providing a novel framework for understanding how climate trends in both mean and variance vary with NPP over time. Nonlinearities in these functions predict whether an increase in climate variance will have a positive effect (convex nonlinearity) or negative effect (concave nonlinearity) on NPP. The influence of climate variance may be particularly intense at ecosystem transition zones, if species reach physiological thresholds that create nonlinearities at these ecotones. Long-term data collected at the confluence of three dryland ecosystems in central New Mexico revealed that each ecosystem exhibited a unique climate sensitivity function that was consistent with long-term vegetation change occurring at their ecotones. Our analysis suggests that rising temperatures in drylands could alter the nonlinearities that determine the relative costs and benefits of varia
Plant aboveground biomass data: BAC: Biodiversity and Climate (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/124/5, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cdr/386/8. The abstract below was extracted from the Level 0 data package and is included for context: Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/325/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed
Baltimore Ecosystem Study: Stream metabolism data for core sites in Gwynns Falls
An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the integrated ecosystem functioning of Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the bioreactivity of streams in our watersheds in an attempt to quantify how streams themselves may affect or be affected by water quality. To assess the bioreactivity of streams, we measure whole stream metabolism, which is an integrative metric which quantifies the production and consumption of energy by a stream ecosystem. Stream metabolism represents how energy is created (primary production) and used (respiration) within a stream; it can be thought of as a stream breathing, with primary production being similar to an inhale, and respiration as an exhale. We are monitoring stream metabolism in each of our long-term water quality monitoring stations by deploying sensors that record dissolve oxygen and temperature of the stream every five minutes, and we also have deployed light sensors to record irradiance every five minutes at long-term BES water chemistry streams, which is needed for metabolism modeling. In addition, each dissolved oxygen sensor is located near a USGS gage which estimates discharge every 15 minutes. We used USGS manual discharge estimations linked with
Raw D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Growing Season D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Average D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Soil data for cores from a transect from the center of the BBC collapse scar into the surrounding burn
This data set contains soil data for cores from a transect from the center of the BBC collapse scar (0 m) into the surrounding burn (30 m). Thirty-five cores were collected soil cores along the transect in March 2003. We drilled cores using a gasoline powered, permafrost corer while soils were frozen. Two to four cores were drilled every 3 m along the transect, yielding a total of 35 cores. We stored cores frozen and cut sample sections using a radial saw. Cores were sampled at the interfaces between different soil layers. We classified soils using the Canadian Soil Classification system (Soil Classification Working Group 1998) identifying fibric, mesic, and humic organic horizions and the A and C mineral horizons. Nine cores were sampled only to the mineral boundary. We measured bulk density, %C and %N for all soil samples. The pH of sample was determined using litmus paper. We oven-dried at 50 - 65 degC and ground all samples before analysis. We analyzed samples for %C and %N using a Carlo Erba EA1108 CHNS analyzer (CE Instruments, Milan, Italy) and a COSTECH ECS 4010 CHNS-O analyzer (Costech Analytical Technologies Inc., Valencia, CA,USA). Sample standard errors were +/- 0.01% for nitrogen, +/- 0.45% for carbon. To indicate fire events in the surrounding ecosystem, charcoal layers in the cores were quantified. We estimated charcoal by emptying dried samples of a known volume and depth (on mean 4.5 cm3) over a 10 cm x 10 cm grid and counting macroscopic charcoal fragments (greater than 0.05 mm in diameter) in each cm grid cell.
Bonanza Creek moisture gradient soil core data: 2004.
This dataset contains the results of a soil sampling effort undertaken in 2004 along a landscape gradient within the Bonanza Creek LTER. Soils were sampled at 5 sites located within unique vegetative environments ranging from a wetland to an upland forest over a distance of 260 m. Data includes, but is not limited to percent carbon, percent nitrogen, 13C of the gas phase, 14C of the gas phase, 13C of the solid phase, bulk density, CO2 flux and DOC.
Bonanza Creek moisture gradient soil core data: 2005.
This dataset contains the results of a soil sampling effort undertaken in 2005 along a landscape gradient within the Bonanza Creek LTER. Soils were sampled at 5 sites located within unique vegetative environments ranging from a wetland to an upland forest over a distance of 260 m. Data includes, but is not limited to percent carbon, percent nitrogen, 13C of the gas phase, 14C of the gas phase, 13C of the solid phase, bulk density, CO2 flux and DOC
Bonanza Creek LTER GIS Data: Core Research Plot Locations
This data package contains the point locations for the core research plots maintained by the Bonanza Creek LTER program. Geospatial_Data_Presentation_Form: vector digital data.
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/346/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
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.