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10,699 results for “easting”
South East Australian Coastal Ocean Forecast System (SEA-COFS)
<p>A suite of high resolution hydrodynamic ocean models for south eastern Australia that form the South East Australian Coastal Ocean Forecast System (SEA-COFS).<br> <br> The modelling suite includes output from several different configurations of the Regional Ocean Modeling System hydrodynamic simulation of the East Australian Current (EAC) System. These various model configurations include free running hindcast models, data assimilating state estimates, ocean forecasts and various nested high resolution runs. There is also a biogeochemical configuration of the fennel model on the EAC parent grid.<br> <br> At the time of upload there were four model grids (check later versions for new and revised / extended grids).</p> <p>The SEA-COFS domain covers the southeastern Australia oceanic region from 25.1-41.5 S and 147.1-162.2 E.</p> <p><br> <strong>SEACOFS_EAC_Grid.nc</strong> Is the parent grid of the EAC Domain 2.5-6km resolution</p> <ul> <li>Kerry, C. G. and M. Roughan, (2020). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5e683944e1369"> 10.26190/5e683944e1369</a>. </li> <li>Kerry, C. G. and M. Roughan,<strong> </strong>Powell, Brian, Oke, Peter (2020). A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012-2013). Version 2017. UNSW.dataset. <a href="https://doi.org/10.26190/5ebe1f389dd87"> 10.26190/5ebe1f389dd87</a>. <br> Kerry, C. , Powell, B. Roughan, M. and Oke, P. (2016) <a href="http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf">Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation</a>. Geosci. Model Dev, 9, 3779-3801, 10.5194/gmd-9-3779-2016<br> </li> </ul> <p><strong>SEACOFS_CoffsHarbour_Grid.nc</strong> Coffs Harbour Region 0.75-1km resolution</p> <ul> <li>Kerry, C., Roughan, M., & Powell, B. (2020). <a href="https://doi.org/10.1016/j.jmarsys.2019.103286">Predicting the submesoscale circulation inshore of the East Australian Current</a>. Journal of Marine Systems (Vol. 204, p. 103286)</li> </ul> <p><strong>SEACOFS_HSM_Grid.nc</strong> - Hawkesbury Shelf Model (HSM) 750m resolution </p> <ul> <li>Ribbat N., M. Roughan, B. Powell, C. Kerry, S. Rao, (2020). A high-resolution (750m) free-running hydrodynamic simulation of the Hawkesbury Shelf region off Southeastern Australia (2012-2013) using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5ec35ca34752e">DOI: 10.26190/5ec35ca34752e</a>. <a href="https://researchdata.ands.org.au/high-resolution-750m-ocean-modeling/1460879">Data access and more information.</a></li> </ul> <p><strong>SEACOFS_Narooma_Grid.nc </strong> Narooma Model 0.75-1km resolution<br> <br> <strong>EAC_BGC Fennel Model </strong></p> <ul> <li>Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., & Kerry, C. (2019) <a href="https://doi.org/10.5194/gmd-12-441-2019">A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system</a> Geosci. Model Dev., 12, 441-456</li> </ul> <p> </p>
Monitoring of Microtus ochrogaster and Microtus pennsylvanicus populations in three different habitats in east-central Illinois, 1972 to 1997.
Populations of 2 species of arvicoline rodents, the prairie vole (Microtus ochrogaster) and meadow vole (Microtus pennsylvanicus), were monitored monthly from 1972-1997 in three distinct habitats: restored tallgrass prairie, bluegrass (Poa pratensis) and alfalfa (Medicago sativa). The study sites were located in the University of Illinois Biological Research Area (Phillips Tract) and Trelease Prairie. Tallgrass prairie was the original habitat of both species in Illinois. Bluegrass, an introduced species, represents the more common habitat in which the two species can be found today in Illinois. Alfalfa, an atypical habitat, provides an abundant source of high-quality food for both species. At each station, one wooden multiple-capture live-trap was placed. Every month, a two-day period of prebaiting was followed by a 3-day trapping session. The data include the species, individual identification, grid station, sex, reproductive status and body mass. Over the span of 25 years, three trapping sessions monthly were conducted to cover the three habitats, dedicating three weeks each month. Several papers have been based on these data.
Meteorology and soil moisture data collected at multiple frequencies from the T-EAST NPP site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's T-EAST NPP site weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind at 5-minute frequency, and all sensors are measured at 30-minute, hourly and daily frequencies. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; total precipitation; average and total solar incoming and reflectance; average albedo; average net radiation; soil moisture, temperature and conductivity. These are measured and calculated based on 1-second scan rate of all sensors located at an automated weather station, and a nearby soil substation, installed at the site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, air temperature and relative humidity at approximately 2.5m, and solar at approximately 3m. Soil sensors are installed at approximately 10, 20 and 30cm depths.
Daily Meteorological data from East Peak, Sabana the Luquillo Experimental Forest
This data publication contains daily means from laser ceilometer data from Sabana and the mountain top station of East Peak, additional data for ozone data from Bisley, and meteorological data collected from Bisley are available from the USDA Forest Service Data Archive at https://doi.org/10.2737/RDS-2022-0050 All sites are located in the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2002 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. A subset of these data are here: https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
High-frequency, hourly, and daily measurements from the East Lake Bonney "Blue Box" (ELBBB), McMurdo Dry Valleys, Antarctica (1994-2024, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research program, continuous measurements have been collected from a network of automated lake monitoring platforms in the McMurdo Dry Valleys of Antarctica. These lake-based stations, commonly referred to as “blue boxes” due to their historically blue instrument enclosures, provide high-frequency observations of key limnological variables essential for understanding physical drivers of aquatic ecosystem dynamics in the perennially ice-covered, meromictic lakes of this polar desert environment. This data package includes measurements from the East Lake Bonney “blue box” lake monitoring platform (ELBBB), established during the 1994-1995 austral summer in East Lake Bonney, Taylor Valley. Parameters include air and water temperature, surface and underwater photosynthetically active radiation (PAR), continuous stage (lake level), ice surface ablation, and, beginning in the 2022-2023 austral summer, dissolved oxygen (DO), reported as percent saturation due to the unavailability of continuous salinity measurements required for computing concentration. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. As there is no universally accepted standard for handling gaps in time-series data, users are encouraged to use the high-frequency data and establish their own criteria for acceptable data completeness to minimize potential bias.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore East Lake Bonney (NELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore East Lake Bonney SLIME transect is located approximately 2000 m west of the Priscu Stream inflow to the East Lobe of Lake Bonney. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore East Lake Bonney (SELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2017-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
N fertilization and recovery experiment (2-4-6) plant species composition data for East of Tvan from 1997 to 2017, yearly
The realization that anthropogenic nitrogen (N) deposition is causing significant environmental change in many ecosystems has led to lower emissions of reactive N and deposition rates in many regions. However, the impacts of N deposition on terrestrial ecosystems can be long-lasting, with significant inertia in the return of the biota and biogeochemical processes to baseline levels. To better understand patterns of recovery and the factors that may contribute to slow or no responses following declines in N deposition, we followed plant species composition, microbial abundance, N cycling rates, soil pH, and pools of NO3- and extractable cations in an impacted alpine ecosystem following cessation of 12-year experiment increasing N deposition rates by 0, 20, 40, and 60 kg N/ha/yr. Simulated N deposition had resulted in a tripling in the cover of the nitrophilic species Carex rupestris, while the dominant sedge Kobresia myosuroides had decreased by more than half at the highest N input level. In addition nitrification rates were elevated, soil extractable magnesium (Mg2+) and pH decreased, and aluminum (Al3+) and manganese (Mn2+) were elevated at the highest N treatment inputs. Over the nine years following cessation of N additions to the impacted plots only the cover of the nitrophilic C. rupestris showed any recovery to prior levels. Abundances of both bacteria and fungi were lower with N addition in both treatment and recovery plots. Rates of nitrification and pools of NO3- remained elevated in the recovery plots, likely contributing to the lack of biotic response to the cessation of N inputs. In addition, nutrient base cations (Ca2+ and Mg2+) and soil pH remained depressed, and the toxic metal cations (Al3+ and Mn2+) remained elevated in recovery plots, also potentially influencing biotic recovery. These results emphasize the importance of considering long-term environmental impacts of N deposition associated with legacy effects, such as elevated N cycling and losse
SBC LTER: Land: Hydrology: Precipitation at Arroyo Hondo at East Avocado Orchard (HO201)
Precipitation was collected at Arroyo Hondo at East Avocado Orchard in the Santa Barbara coastal area (site ID: HO201). A Tipping Bucket rain gauge from either Qualimetrics (Model 6011B) or Sutron (Model 5600-0425-2) was used. Data are reported hourly, and times reflect the end of the each 1-hour interval.
Neolithic/Copper Age radiocarbon dates from West Carpathian basin and East Alps
<p>Radiocarbon dates used in this study were collected from original publications including grey literature. The resulting database was cross-referenced with available radiocarbon databases to ensure the quality of data. We used the latest revision (2019) of CalPal database by Bernhardt Weninger (<em>B. Weninger et al.</em> 2019), EuroEvol database (<em>Manning et al.</em>2016), C14.sk database (<em>Barta et al.</em> 2013) and RADON database (<em>Martin Hinz et al.</em> 2012). </p> <p> </p> <p>Database structure consist of ten columns, from site name (Site), unique site code (Scode), site longitude and latitude (Lon and Lat) in degrees, country (Country), laboratory code (Labcode), the conventional radiocarbon date (Date) in years before present, standard deviation (SD), dated material (Mat) and bibliographic reference (Cit). Where possible, we referenced the radiocarbon database where the date is recorded, otherwise, the publication where the date is reported or referenced is cited. </p> <p> </p> <p>Many thanks to Bernhardt Weninger for kindly providing the latest version of CalPal database.</p> <p> </p> <p><strong>References </strong></p> <p> </p> <p>B. Weninger, Jöris O., Danzeglocke U. 2019. CalPal-2007. Cologne Radiocarbon & Palaeoclimatic Research Package. <em>http:\\www.calpal.de</em></p> <p>Manning K., Colledge S., Crema E., Shennan S., Timpson A. 2016. The Cultural Evolution of Neolithic Europe. EUROEVOL Dataset 1: Sites, Phases and Radiocarbon Data. <em>Journal of Open Archaeological Data </em>5:e2. DOI: http://doi.org/10.5334/joad.40</p> <p>Martin Hinz M.F., Johannes Müller, Dirk Raetzel-Fabian, Rinne C., Sjögren K.-G., Wotzka H.-P. 2012. RADON - Radiocarbon dates online 2012. Central European database of 14C dates for the Neolithic and Early Bronze Age. <em>http:\\www.jungsteinsite.de</em></p> <p>Barta P., Demján P., Hladíková K., Kmeťová P., Piatničková K. 2013. Database of radiocarbon dates measured on archaeological samples from Slovakia, Czechia, and adjacent regions. <em>http://www.c14.sk</em></p>
Detecting East Asian Prejudice on Social Media
<p>This repository contains:</p> <ul> <li>A deep learning model which distinguishes between Hostililty against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Neutral content. The F1 score is 0.83.</li> <li>A detailed annotation codebook used for marking up the tweets.</li> <li>A labelled dataset with 20,000 entries.</li> <li>A dataset with all 40,000 annotations, which can be used to investigate annotation processes for abusive content moderation.</li> <li>A list of thematic hashtag replacements.</li> <li>Three sets of annotations for the 1,000 most used hashtags in the original database of COVID-19 related tweets. Hashtags were annotated for COVID-19 relevance, East Asian relevance and stance.</li> </ul> <p>The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. This data repository is for a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.</p> <ul> </ul> <p>This work is a collaboration between The Alan Turing Institute and the Oxford Internet Institute. It was funded by the Criminal JusticeTheme of the Alan Turing Institute under Wave 1 of The UKRI Strategic Priorities Fund, EPSRC Grant EP/T001569/1</p>
Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.
<p>Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला). View of the ghāṭ from the north east, with resevoir, showing part of the modern dam.</p>
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
East Asian calendar conversion database
<p>SQL dump (MySQL 5.x) from a database for converting East Asian (here: Chinese, Japanese, Korean) calendars. The data is also available for download at http://authority.dila.edu.tw/docs/open_content/download.php, where separate datasets for Chinese, Korean, and Japanese calendars are available. An interface for querying the data is here: http://authority.dila.edu.tw/time/.</p> <p>Using the Julian Day as common standard it allows mapping of East Asian calendar dates to the Julian, proleptic Gregorian, and Gregorian calendar. It is the currently largest and most detailed open access dataset for this purpose. The database was compiled between 2008 and 2011 at the Dharma Drum Institute of Liberal Arts, Jinshan, Taiwan. The data for the Japanese calendar is based on material provided by Takashi Suga.</p> <p>A publication describing the dataset is Marcus BINGENHEIMER, Jen-Jou HUNG, Simon WILES, Boyong ZHANG. “Modeling East Asian Calendars in an Open Source Authority Database.”<em> International Journal of Humanities and Arts Computing</em> Vol. 10-2, pp. 127-144. DOI: 10.3366/ijhac.2016.0164.</p>
O_WESTERSCHELDE - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding in East Flanders (Belgium)
<p><em>O_WESTERSCHELDE - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding in East Flanders (Belgium)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>O_WESTERSCHELDE</strong>, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, <a href="http://www.uva-bits.nl">http://www.uva-bits.nl</a>). The study was operational from 2018 to 2020. In total 13 individuals of Eurasian oystercatchers (<em>Haematopus ostralegus</em>) have been tagged in their breeding area in East Flanders (Belgium), west of the river Scheldt, mainly to study their habitat use on mudflats of the Western Scheldt (the Netherlands). Data are uploaded from the UvA-BiTS database to Movebank and from there archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>). No new data are expected.</p> <p>See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1099562810">1099562810</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/10053702/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>O_WESTERSCHELDE-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>O_WESTERSCHELDE-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> <li><strong>O_WESTERSCHELDE-acceleration-yyyy.csv.gz</strong>: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. Additional funding was provided by the Sovon Dutch Centre for Field Ornithology.</p>
Phlorest phylogeny derived from Kitchen et al. 2009 'Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kitchen A, Ehret C, Assefa S & Mulligan CJ. 2009. Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Proceedings of the Royal Society B: Biological Sciences, 270(1668), 2703-2710.</p> </blockquote>
Subglacial valleys preserved in the highlands of south and east Greenland record restricted ice extent during past warmer climates: Datasets
<p>This dataset contains the following files:</p><ul><li><strong>mountain_glacial_valleys_2km.shp (and ancillary files: .dbf, .prj, .shx)</strong>: ESRI shapefile of the subglacial valleys mapped along the southern and eastern highlands of Greenland using MODIS Mosaic of Greenland imagery and radio-echo sounding data.</li><li><strong>mountain_glacial_limit.shp (and ancillary files: .dbf, .prj, .shx)</strong>: ESRI shapefile of the interpreted palaeo-glacial valley limit along the southern and eastern highlands of Greenland based on the distribution and morphology of the mapped subglacial valleys.</li><li><strong>ex_g5km_10ka_hy_east.nc</strong>: NetCDF file of the Parallel Ice Sheet Model output spatially variable fields for the best-fitting simulation for the eastern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li><strong>ts_g5km_10ka_hy_east.nc</strong>: NetCDF file of the Parallel Ice Sheet Model output scalar time series for the best-fitting simulation for the eastern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li><strong>ex_g5km_10ka_hy_south.nc</strong>: NetCDF file of the Parallel Ice Sheet Model output spatially variable fields for the best-fitting simulation for the southern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li><strong>ts_g5km_10ka_hy_south.nc</strong>: NetCDF file of the Parallel Ice Sheet Model output scalar time series for the best-fitting simulation for the southern highlands (run at 5 km horizontal resolution for 10,000 model years).</li></ul>
Carbonate Chemistry and δ18O-H2O from East and West Greenland fjords, August 2018 and 2016
<p>Greenland’s fjords and coastal waters are highly productive and sustain important fisheries, but retreating glaciers and increasing meltwater supply are changing fjord circulation and biogeochemistry, which may threaten the future productivity of these unique ecosystems. The freshening of Greenland fjords caused by unprecedented melting of the Greenland Ice Sheet has the potential to alter carbonate chemistry in coastal waters, which would influence CO<sub>2</sub> uptake as well as have biological consequences from acidification. However, few studies to date explore the current acidification state in Greenland coastal waters. Here we present the first-ever large-scale measurements of carbonate system parameters and δ<sup>18</sup>O measurements in 16 Greenlandic fjords and by combining datasets from two August cruises. HDMS Lauge Koch and RV Sanna cruises sampled on the East and West coast of Greenland in August 2018 and 2016 respectively. This dataset consists of 52 carbonate chemistry sample sites where dissolved inorganic carbon (DIC), total alkalinity (TA), and δ<sup>18</sup>O-H<sub>2</sub>O were measured throughout the water column. Water samples were collected in Niskin bottles and were transferred directly into triplicate 12 ml exetainers with a gas tight Tygon tubes, allowing overflow of at least 3 times the volume of the exetainer. Triplicate exetainers were collected for both DIC and TA at each depth. Samples were preserved with HgCl<sub>2</sub> (saturated solution) to a final concentration of 0.02%. TA was measured on an Apollo SciTech AS-ALK2 total alkalinity titrator based on the Gran titration procedure for samples in West Greenland. While samples for East Greenland were measured on automatic titrator (Metrohm 888 Titrando), and a combined Metrohm glass electrode (Unitrode). DIC samples were analyzed on Apollo SciTech's AS-C3 analyzer for both cruises, using a sample volume of 0.5 ml. Routine analysis of Certified Reference Materials (provided by A. G. Dickson, Scripps Institution of Oceanography) verified that the accuracy of DIC and TA measurements. Coalescing this dataset therefore provides the first-ever analysis of wide-scale Greenland Fjord carbonate chemistry.</p> <p> </p> <p>Environmental variables recorded by CTD instruments are available for cruises from the West and East coasts respectively at the following DOIs: <a href="https://doi.org/10.5281/zenodo.4062024">https://doi.org/10.5281/zenodo.4062024</a> and <a href="https://doi.org/10.5281/zenodo.5572329">https://doi.org/10.5281/zenodo.5572329</a>.</p> <p> </p> <p>We would like to thank the crew on board the HDMS Lauge Koch and RV Sanna for their collaboration. The Cruises were funded by Danish Centre for Marine Science (Grants: 2016-05 and 2017-06) and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic. This dataset is a contribution the project FACE-IT (The Future of Arctic Coastal Ecosystems – Identifying Transitions in Fjord Systems and Adjacent Coastal Areas). FACE-IT has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869154.</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Bathymetry beneath the Amery ice shelf, East Antarctica, revealed by airborne gravity
<p>We estimated the seafloor topography beneath the Amery Ice Shelf, East Antarctica, from airborne gravity anomaly through a nonlinear inversion method called simulated annealing. The estimation results provide a view of the seafloor beneath the Amery Ice Shelf, where direct bathymetric observations are rare. The model, 'gravity_estimated_seafloor_topography_beneath_the_Amery_Ice_Shelf.nc', is in NetCDF format which can be read through MATLAB commands "ncdisp" and "ncread". Contents of the model can be found in "contents.txt". The MATLAB program "nc2mat.m" reads the NetCDF ".nc" format model and saves the variables in the model to a MATLAB ".mat" format file.</p>
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.