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2,021 results for “valley”
Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)
<p>Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)</p> <p>- Main ore minerals: pyrite, chalcopyrite, siderite, aragonite</p> <p>- Provisional References:</p> <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>
Continuous phylogeography analysis of Rift Valley fever virus (RVFV) in Africa and the Arabian Peninsula
<p>Spatiotemporal-explicit Bayesian phylogenetic trees and MCMC log files for Rift Valley fever virus large, medium and non-structural genetic sequences generated by BEAST v1.10</p> <ol> <li>Log files (logs.zip)</li> <li>Tree files (trees.zip)</li> </ol>
Chug valley flora and fauna
<p><strong>Short description: </strong>This collection of videos, audio and photo files displays the flora and fauna of the Chug valley between 2012 and 2017. In particular, it focuses on the species that are used for various purposes. The following sections describe the hunting practice and the collection of vegetable forest produce. The translated, transcribed and glossed texts and accompanying sound files describe the past practices of collecting beeswax and collecting stinging nettle, and the contemporary practices of collecting tadpoles, frogs and fish; collecting giant hornets; hunting takin; and hunting porcupine.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Glacier inventory of the upper Huasco valley, Norte Chico, Chile
<p>Shapefile of the glacier inventory of the Upper Huasco catchment, Chile, that was generated for the following research article:</p> <p>Nicholson, L. I., Marín, J., Lopez, D., Rabatel, A., Bown, F. and Rivera, A.: Glacier inventory of the upper Huasco valley, Norte Chico, Chile: glacier characteristics, glacier change and comparison with central Chile, Ann. Glaciol., 50(53), 111–118, doi:10.3189/172756410790595787, 2010.</p> <p>Glaciers were mapped on the basis of ASTER imagery from 2004, and classifed for the study as clean ice glaciers (1), debris covered glaciers (2) and rock glaciers (3). Additionally, classification following the GLIMS gudeilines (https://www.glims.org/.../GLIMS_Glacier-Classification-Manual_V1_2005-02-10.pdf ) was applied where possible to glacier features on the basis of additional ASTER imagery from 2002 and 2003, and older air photographs.</p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"
<p><em>Part 2</em> of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>).</p> <p>Due to memory constraints, the supplementary material consists of two parts:</p> <ul> <li><em><strong>Part 1: </strong></em>(available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the <em>0.5-km</em> simulation.</li> <li><em><strong>Part 2: </strong></em>Includes the preprocessed AROME model output (NetCDF files) of the <em>1.0-km </em>and <em>2.5-km</em> simulations (see description below).</li> </ul> <p>To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). <br>The Python scripts should be placed in the same parent folder because some of them depend on each other <strong>(!! Important !!).</strong><br>Original AROME model output files (GRIB2 format) are not published due to their large size.</p> <p>The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal<br>grid spacing:</p> <ul> <li><strong><em>OP2500:</em></strong> for 2.5 km</li> <li><em><strong>OP1000: </strong></em>for 1.0 km</li> <li><em><strong>OP500:</strong></em> for 0.5 km</li> </ul> <h3><strong>Datasets Part 2</strong></h3> <p>Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains:</p> <ul> <li><strong>datasets_OP1000.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>1.0-km</em> simulation. </li> <li><strong>datasets_OP2500.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>2.5-km</em> simulation. </li> </ul> <p>The datasets of the<em> 0.5-km</em> simulation (<strong>datasets_OP500.tar.xz</strong>)<strong> </strong>can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>).<br>The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code><em><strong>.tar.xz</strong></em></code> files.<br>The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines.<br>The provided NetCDF datasets are preprocessed from the <em>GRIB2</em> output of the AROME-Aut simulations. <br>For the scripts to function properly, you need to adjust the path to the datasets within <code><strong>path_handling.py</strong></code><strong>.</strong></p> <p>Each <strong><code>datasets_OP*.tar.xz</code></strong> file contains NetCDF files for different type of levels: <em>surface, hybridPressure </em>(model levels)<em>, isobaricInhPa </em>(pressure levels),<em> meanSea </em>(mean sea level)<em>, heightAboveGround </em>(constant height levels)<em>. </em> The following naming convention for the datasets is used:</p> <ul> <li><strong>ds_OP*_<em>var</em>_hybridPressure_<em>[lon1, lon2, lat1, lat2]</em>.nc</strong>: Contains data on <em>hybrid pressure model levels</em> for a specific variable (<em>var</em>; e.g., <em>u, v, z, pres, q, t</em>) for the geographical extent defined in the brackets. </li> <li><strong>ds_OP*_interp_hybridPressure_<em>(lon,lat)</em>.nc</strong>: Combined dataset on <em>hybrid pressure model levels.</em> The data is bilinearly interpolated to the specified location <em>(lon, lat)</em>.</li> <li><strong>ds_OP*_<em>var</em>_surface_<em>whole</em>.nc</strong>: Contains data on <em>model surface </em>for a specified variable (<em>var</em>; e.g., <em>z, sp, t, tcc</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_heightAboveGround_instant_<em>whole</em>.nc</strong>: Combined dataset on <em>height levels </em>(e.g., <em>2-m and 10-m</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_<em>var</em>_meanSea_<em>whole</em>.nc</strong>: Contains data on <em>mean sea level</em> for specified variable (<em>var;</em> e.g., <em>prmsl</em>) for the <em>whole</em> available domain extent. </li> </ul> <p>The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.</p>
Eleven years of training data for south foehn for two valleys in the Eastern Alps
<p>This south foehn training data is suited for machine learning purposes. </p> <p>It was created by applying objective foehn classification (OFC, Vergeiner 2004) on hourly data of various stations in the Eastern Alps in Austria. The two valleys Rhine and Inn and two intensities are available on a daily basis, where</p> <ul> <li>0.0 means no foehn on that day,</li> <li>0.5 means localised foehn on that day (at least one third and up to half the stations in the region responded to OFC),</li> <li>1.0 means widespread foehn on that day (more than half the stations in the region responded to OFC),</li> </ul> <p>provided for each valley individually.</p> <p>A paper, where the process of creation is described, is in preperation and will be linked as soon as it is reviewed. </p>
Belham River Valley Digital Surface Model (1 metre) - March 2019 - Pleiades Photogrammetry (co-registered)
<p>This tiff contains a Digital Surface Model (DSM) of the Belham River Valley in Montserrat, Eastern Caribbean. This was generated via leverage of Pleiades tri-stereo imagery throught the DSM-OPT software platform. This DSM has been co-registered to a LiDAR DSM (generated in 2010, vertical error 0.15 m) and found to have a vertical error of 2.3 m.</p>
Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
<p>Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of <em>"Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window"</em> by Chen et al. (2021).</p> <p>This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.</p>
Bathymetry and Sediment thickness distribution of Lago dei Seracchi alpine lake, Rutor basin, Aosta Valley, Italy
<p>Maps of water depth and sediment accumulation in an Italian proglacial lake, done by Ground Penetrating Radar (GPR) in July 2021. Supporting Time domain reflectometry surveys and geotechnical analyses on the sediments are also provided. For details, see the readme file in the dataset folder.</p>
Tryps-IN: A streamlined palaeoproteomics workflow enables ZooMS analysis of 10,000-year-old petrous bones from Jordan rift-valley
<p>Poor preservation of collagen in dry and/or arid environments has hindered the application of Zooarchaeology by mass spectrometry (ZooMS) analysis in many regions of the world, and as a result many zooarchaeological investigations have relied exclusively on the morphological assessment of fragmentary remains, due to the inadequate preservation of biomolecules. The climatic conditions of Southwest Asia include extreme temperature fluctuations unconducive to preservation of proteins and DNA. We performed zooarchaeological analysis of remains from the 10,000-year-old site of Shkārat Msaied in Jordan and sub-sampled twenty-eight petrous bones, the hardest bone in the mammalian skeleton, for species identification by ZooMS. Using an unconventional and simplified extraction protocol we call Tryps-IN, in which digestion was performed without removal of the demineralising EDTA, we taxonomically identified several fragments, outperforming the established ZooMS work-flow. A subset of identifications was subsequently confirmed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) protein sequencing. The new methodology presented here opens the possibility of further bioarchaeological investigation of other fragmentary faunal assemblages within this region of archaeological significance. </p>
Non-identical moire twins in bilayer graphene revealed by valley Hall effect measurements
<p>The superlattice obtained by aligning a monolayer graphene and boron nitride (BN) inherits from the hexagonal lattice a sixty degrees periodicity with the layer alignment. It implies that, in principle, the properties of the heterostructure must be identical for 0$^{\circ}$ and 60$^{\circ}$ of layer alignment. Here, we demonstrate, using dynamically rotatable van der Waals heterostructures, that the moir\'e superlattice formed in a bilayer graphene/BN has different electronic properties at 0$^{\circ}$ and 60$^{\circ}$ of alignment. Although the existence of these non-identical moir\'e twins is explained by different relaxation of the atomic structures for each alignment, the origin of the observed valley Hall effect remains to be explained. A simple Berry curvature argument do not hold to explain the hundred and twenty degrees periodicity of this observation. Our results highlight the complexity of the interplay between mechanical and electronic properties on moir\'e structure and the importance of taking into account atomic structure relaxation to understand its electronic properties.</p>
Iron Age Funnelpendant Lower Rhine Valley
<p>Catalog of the Funnelpendant found in the Lower Rhine Valley.</p> <p>This dataset was transformed into <a href="https://arkeogis.org/en/">ArkeoGIS</a> format during a Summer School on Research Data Management in Pre- and Protohistoric Archaeology at Kiel University co-organised by <a href="https://www.sfb1266.uni-kiel.de/en">CRC1266</a> and <a href="https://www.cluster-roots.uni-kiel.de/en?set_language=en">Cluster of Excellence ROOTS</a>. Conducted from 10-14th of July 2023.</p> <p>- artefacts_Nakoinz2004_Trichteranhaenger_epsg25832.csv: original data, digitalised from analog source</p> <p>- artefacts_Nakoinz2004_Trichteranhaenger_epsg25832.yaml: metadata</p> <p>- ArkeoGIS_Nakoinz_funnelpendant_2004_v2.csv: transformed data to match ArkeoGIS structure (in English)</p>
Data on the fleas of house mice and ship rats in the Orongorongo Valley, New Zealand
<p>Data on autopsies and fleas of individual rats and mice snap-trapped in 80 quarterly 3-day trapping sessions over 1971-1991 in the Orongorongo Valley, Wellington, New Zealand. The fleas are preserved as slides in the Pilgrim Collection of Te Papa Tongarewa Museum of New Zealand, Wellington. The data are analysed in a forthcoming paper by Fitzgerald, Efford and Karl.</p> <p>'Autopsy data.csv' is the main spreadsheet.</p> <p>'Data format.csv' defines columns and codes.</p> <p>The autopsy data are a subset of those reported by Fitzgerald, Efford<br> and Karl (2004) and Efford, Fitzgerald, Karl and Berben (2006), both<br> with respect to the sampling period (trapping continued after May 1991,<br> but rats and mice were not searched for fleas) and data columns<br> (some reproductive fields have been omitted).</p> <p>The autopsy data may be read from the csv file into R (R Core Team 2023)<br> with the statement:</p> <p>autopsyF <- read.csv(file = 'Orongorongo fleas 1971-1991 Autopsy data.csv')</p> <p>The resulting dataframe has 31 columns and 3006 rows:</p> <p>names(autopsyF)<br> [1] "Autopsy" "Line" "Date" "Analysis.Year" "Session" <br> [6] "Day" "Season" "Trap" "Traptype" "Alive.dead" <br> [11] "Species" "Sex" "Age" "Weight" "TotLth" <br> [16] "TailLth" "Fleas" "LsegnisM" "LsegnisF" "LsegnisU" <br> [21] "Lsegnis" "NfascM" "NfascF" "NfascU" "Nfasc" <br> [26] "Vagina" "Lact" "Uterus" "EmbryosT" "ScarCond" <br> [31] "Testes" <br> <br> nrow(autopsyF)<br> [1] 3006</p> <p>For completeness, the autopsy data include 64 hosts from these trapping sessions that were<br> not searched for fleas (coded as Fleas = 2).</p> <p>Quarterly indices of host density were tabulated by Fitzgerald et al. (2004; mice) and<br> Efford et al. (2006; rats).</p> <p>References</p> <p>Efford MG, Fitzgerald BM, Karl BJ, Berben PH. 2006. Population dynamics<br> of the ship rat <em>Rattus rattus</em> L. in the Orongorongo Valley, New Zealand.<br> New Zealand Journal of Zoology 33:273-297.</p> <p>Fitzgerald BM, Efford MG, Karl BJ. 2004. Breeding of house mice and the<br> mast seeding of southern beeches in the Orongorongo Valley, New Zealand.<br> New Zealand Journal of Zoology 31:167-184.</p> <p>Fitzgerald BM, Efford MG, Karl BJ. 2023. The fleas of house mice (<em>Mus musculus</em>)<br> and ship rats (<em>Rattus rattus</em>) in forest of the Orongorongo Valley, New Zealand.<br> For submission to New Zealand Journal of Zoology.</p> <p>R Core Team 2023. R: A Language and Environment for Statistical Computing.<br> R Foundation for Statistical Computing, Vienna, Austria.<br> https://www.R-project.org/</p>
Subglacial valleys and ridges of Highland A, East Antarctica
<p>This dataset contains 3 GIS shapefiles which delineate a landscape buried beneath the ice in East Antarctica. The files are: PlateauBoundaries (a polygon shapefile outlining the extent of 3 blocks of ancient land surface), RADARSATRidges (a line shapefile delineating inferred mountain ridgelines inside each block of ancient landsurface), and RADARSATValleys (a line shapefile delineating inferred valley thalwegs inside each block of ancient landsurface). The landscape was mapped from RADARSAT satellite data of the surface of the East Antarctic Ice Sheet. This dataset links to a 2023 paper by the same authors entitled 'An ancient river landscape preserved beneath the East Antarctic Ice Sheet'.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD AWS dataset for the Inn Valley exit area
<p>This dataset contains data measured at 5 automatic weather stations operated by the German Meteorological Service (DWD) during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location, instrumentation and measured variables</strong></p> <p>The following table lists the station locations and available measurements. Data of the automatic weather stations is provided from the given start date until 19.10.2022 23:50 at a temporal resolution of 10min. If not noted otherwise, wind, temperature, humidity and pressure are measured at 2m above ground, precipitation at 1m above ground.</p> <table> <thead> <tr> <th scope="col">Station name</th> <th scope="col">start of deployment</th> <th scope="col">location and elevation</th> <th scope="col">instruments</th> <th scope="col">measured variables</th> </tr> </thead> <tbody> <tr> <td>Brannenburg</td> <td>24.06.2022</td> <td> <p>47.742360 N</p> <p>12.121712 E</p> <p>456 m MSL</p> </td> <td> <p>LTS2000, HMP45d, Gill,</p> <p>mSonic3, PTB330, CNR4,</p> <p>Pluvio, HFP01SC</p> </td> <td> <p>U_xm,V_xm,W_xm (2m*,4m,10m*)</p> <p>T, TX, TN, RH, P, RR, GSR, RSR, ALR, SLR</p> </td> </tr> <tr> <td>Flintsbach</td> <td>02.06.2022</td> <td> <p>47.728401 N</p> <p>12.127413 E</p> <p>468 m MSL</p> </td> <td>LTS2000, HMP45d, Thies</td> <td>WDIR, WSPEED, T, TX, TN, RH</td> </tr> <tr> <td>Hohe Asten</td> <td>02.06.2022</td> <td> <p>47.703476 N</p> <p>12.115288 E</p> <p>1226 m MSL</p> </td> <td>LTS2000, HMP45d, Thies</td> <td>WDIR, WSPEED, T, TX, TN, RH</td> </tr> <tr> <td>Kiefersfelden</td> <td>05.06.2022</td> <td> <p>47.607147 N</p> <p>12.202737 E</p> <p>473 m MSL</p> </td> <td> <p>LTS2000, HMP45d, Thies,</p> <p>PTB110</p> </td> <td>WDIR, WSPEED, T, TX, TN, RH, P</td> </tr> <tr> <td>Oberaudorf</td> <td>05.06.2022</td> <td> <p>47.654282 N</p> <p>12.180008 E</p> <p>466 m MSL</p> </td> <td>LTS2000, HMP45d, Thies</td> <td>WDIR, WSPEED, T, TX, TN, RH</td> </tr> </tbody> </table> <p> </p> <p> </p> <p><em>Variable abbreviations: U_xm, V_xm=horizontal wind components xm above ground, W=vertical wind component, WDIR=wind direction [°], WSPEED= wind speed [m/s], T=average temperature [°C], TX=maximum temperature [°C], TN=minimum temperature [°C], RH=relative humidity [%], P=pressure [hPa], GSR=global shortwave radiation [W/m<sup>2</sup>], RSR=reflected shortwave radiation [W/m<sup>2</sup>], ALR=atmospheric longwave radiation [W/m<sup>2</sup>], SLR= surface longwave radiation [W/m<sup>2</sup>]</em></p> <p> </p> <p> </p> <p><strong>* </strong>due to quality issues, wind components in 2 and 10m at Brannenburg will be made available at a later date.</p> <p><strong><em>2. Data corrections</em></strong></p> <p>Basic quality checks were applied to the data. Missing values are indicated by NA.</p> <p><strong>3. Data file structure</strong></p> <p>The data are provided in separate .txt files for each station. Files are tab separated. The first column lists the date in the format year-month-day hour:minutes:second. The following column names correspond to the variables listed in section 1.</p> <p><strong>4. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p>
A micro-environmental dataset exploring shrub-open climate contrasts in Cuyama Valley, California, USA,
A total of 62 Hobo micro-station data logger weather stations were deployed in Cuyama Valley, California drylands on public lands. Each station had 4 channels with two EC5 soil moisture smart sensors and two 12-Bit temperature smart sensors. The sensor arrays were deployed for two years or a total of 24 months. Key ecological variables included microsite, location, vegetation type, and extent of vegetation at each microsite. Temperature sensors were deployed 15 cm above the soil surface under the shrub canopy of Ephedra californica (sheltered from direct sunlight) or at adjacent, open paired-microsites within 1.5m of a focal shrub.Temperatures listed are in degrees Celsius. Soil moisture sensors were inserted into the soil near each temperature probe. The x,y,z columns are the width, perpendicular measure, and height in m respectively of each shrub at those microsites.
Late-Holocene paleofloods in the Upper Little Tennessee River valley, Southern Blue Ridge Mountains, USA.
We derive a paleoflood chronology for the past 2000 years from three stratigraphic sections of overbank sediments with dates from radiocarbon, luminescence, 137Cs techniques, and historical records. Particle sizes were measured in 6-15 year intervals in post-1870 sediments and in 45-170 year intervals in pre-1870 sediments using an automatic laser analyzer. The sedimentological characteristics of ad 1948-2009 deposits were compared with gaging records, demonstrating that fine sand content and sorting discern time intervals of large floods, but flood magnitudes are not well resolved. This modern analog was applied to pre-1870 sediments and revealed two periods in the last 2000 years with large floods during AD 650-850 and AD 1100-1350, which are times when the regional tree-ring record showed extreme wetness and no severe or extreme droughts. Our findings indicate flood-prone phases of transitional climate at the beginning and end of the "Medieval Warm Period" (MWP), and relatively subdued flooding during the "Little Ice Age" (LIA), possibly correlated with rearrangement of macro-scale atmospheric circulation patterns between the MWP and the LIA. (Wang, L. and Leigh, D.S. 2012. Late Holocene paleofloods in the Upper Little Tennessee River Valley, Southern Blue Ridge Mountains, USA. The Holocene, 22(9): 1061-1066. DOI: 10.1177/0959683612437863.)
Hubbard Brook Experimental Forest: Landscape scale (valley-wide) soil carbon and nitrogen cycling data
The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. This dataset includes total soil carbon, nitrogen and organic matter content, potential net nitrogen mineralization and nitrification rates, microbial respiration rates, soil water content and holding capacity, soil ammonium and nitrate concentrations, soil pH, and tree composition in a subset of 100 randomly selected plots in 2000. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. An analysis of these data can be found in: Venterea, R. T., Lovett, G. M., Groffman, P. M., & Schwarz, P. A. (2003). Landscape patterns of net nitrification in a northern hardwood-conifer forest. Soil Science Soc. Amer. J., 67, 527–539. https://doi.org/10.2136/sssaj2003.5270
Stream descriptions, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the the glacial meltwater streams in the region. Measurements are regularly made to examine water quality and quantity, as well as chemical and biological properties. The stream descriptions table serves as an index of the streams that have been studied. It documents the streams' basins and lengths, and provides a brief desciption of the landscape in which the streams are found.
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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.