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424 results for “In-situ”
COLA-hires: High-resolution (0.5x0.625) regional carbon fluxes inferred from in-situ and OCO-2 data
<p>This dataset contains high-resolution CO<sub>2</sub> inversion estimate in North America, East Asia, and Europe at 0.5x0.625 resolution from 2015 to 2018 using the Carbon in Ocean-Land-Atmosphere (COLA) system. The in-situ observations obtained from NOAA obspack and the land-nadir/land-glint retrevials from OCO-2 are assimilated.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – Vertical profiles and Multi-Point In-Situ Measurements at Nafingalm collected with the SWUF-3D UAS fleet
<p>This dataset contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2022 through 27 June 2022 at Nafingalm, Austria with the SWUF-3D fleet. The data was collected in association to the TEAMx-PC22 field campaign. A maximum of three UAS were operated simultaneously. Processed level-2 data is provided. For level-2 data, time synchronization between individual UAS was done through interpolation, if multiple UAS are operated simultaneously.</p> <p>In this dataset vertical profiles (swuf3dvpro) between 10m and 120m above ground level and time series of UAS hovering for approx. 10 minutes at fixed positions (swuf3dhover) are provided with a temporal resolution of 1 Hz.</p> <p>The data are provided in NetCDF format with metadata and variable descriptions in the style of the SAMD Product standard: Jahnke-Bornemann, Annika. (2022, August 18). The SAMD Product Standard (Standardized Atmospheric Measurement Data) (Version 2.2). http://doi.org/10.25592/uhhfdm.10416</p>
In-situ crop phenology dataset from sites in Bulgaria and France
<p><strong>Site description</strong></p> <p>The in-situ crop phenology stages dataset was collected from Bulgarian and French sites for major European crops. Each selected field is described by its precise location and shape.</p> <p>In Bulgaria, seventeen production crop fields were selected: Oborishte (P0 and P3), Gurkovo (P01 and P13), Trigorci (P02), General Kiselovo (P1 and P2), Dobrich (P4 and P18), Mirovci (P14), Neofit Rilski (P15), Boyana (P17) and Gurkovo (P19, P20, P22, P23, P24) in the Northeast part of the Danube plain. Winter rapeseed and wheat were grown in the selected sites. The cultivation follows a crop rotation schema, but the collected in-situ phenology data was from one growing season only per field. The sites are characterized by mostly flat terrain and a moderate continental climate with cold winters and hot summers. The annual cumulative precipitation is 540 mm, and the mean annual temperature is 10.2˚C. The soil has mainly a sandy loam texture.</p> <p>In France, two research fields were selected: Lamasquère (FR-Aur) and Auradé (FR-Lam). The FR-Aur crop site is part of a grain farm located in a hilly area where winter wheat – rapeseed – barley – sunflower crop rotation is used. The FR-Lam site is located in a plain where a winter wheat – irrigated maize crop rotation is practiced. Winter cover crops (faba bean, white mustard) were sometimes introduced on both sites between a winter crop and a summer crop. During the selected time frame all mentioned crop species were cultivated. The climate is temperate with Mediterranean and oceanic influences, with mild winters, rainy springs, and high temperatures over the summer with low rainfall, followed by sunny autumns. The mean annual rainfall over the past 24 years was 617 ± 101 mm and the mean annual temperature was 13.7 ± 0.6 °C on both sites (Beziat et al. 2009, Tallec et al. 2022). According to the textural triangle of Malterre and Alabert (1963), the soil of the FR-Lam site has mainly clay, silt, and sand and FR-Aur is silt, clay, and sand.</p> <p> </p> <p><strong>Crop phenology measurements</strong></p> <p>The Start of the Season (SOS) is represented by a specific phenophase that corresponds to the BBCH10-13 distinguishable phenology stage and the End of the Season (EOS) by the Harvest date.</p> <p>In Bulgaria, crop data (Sowing, Phenophase, and Harvest date) from all fields were collected by visual observations in the field, written in a paper notebook, and subsequently passed to a Microsoft Excel file. The available data for each field is one season and some fields don't have the corresponding phenophase or harvest date.</p> <p>In France, crop data (Sowing and Harvest date) from both fields were collected through visual observation on site, identical to the Bulgaria sites. The phenophase date was determined by visual observations on site and with PhenoCam picture and/or digital photo camera picture analysis. The unique exception is the Lamasquére site in the 2017/2018 and 2019/2020 seasons where the phenophase date was collected through visual observations on site. The available data for both fields have numerous crop seasons.</p> <p> </p> <p><strong>File description</strong></p> <p>The “1_senseco_data_insitu_crop_phenology_Bulgaria_France.txt” concerns all crop phenology measurements (sowing, harvest, and phenophase date) and related information. The “2_senseco_metadata_insitu_crop_phenology_Bulgaria_France.txt” describes all the variables from “1_senseco_data_insitu_crop_phenology_Bulgaria_France.txt”. The "FR_Pictures_JPG.zip" contains all the photos from the French sites. The "georeference_GEOJSON_polygons.zip" contains all the georeferenced shape files from each crop plot and the "georeference_CSV_comma" contains all the georeferenced shape files in an open format. See the file "metadata_geojson_csv.txt" with support data for the georeferenced files.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>Beziat, P., Ceschia, E., Dedieu, G., 2009. Carbon balance of a three crop succession over two cropland sites in South West France. Agricultural and Forest Meteorology 149, 1628–1645. https://doi.org/10.1016/j.agrformet.2009.05.004</p> <p>Malterre, H. and Alabert, M. : Nouvelles observations au sujet d’un mode rationnel de classement des textures des sols et des roches meubles - pratique de l’interprétation des analyses physiques, Bulletin de l’AFES 2, 76-84, 1963.</p> <p>Tallec, T., Bigaignon, L., Delon, C., Brut, A., Ceschia, E., Mordelet, P., Zawilski, B., Granouillac, F., Claverie, N., Fieuzal, R., Lemaire, B., Le Dantec, V., 2022. Dynamics of nitrous oxide emissions from two cropping systems in southwestern France over 5 years: Cross impact analysis of heterogeneous agricultural practices and local climate variability. Agricultural and Forest Meteorology 323, 109093. https://doi.org/10.1016/j.agrformet.2022.109093</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>This research was supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on March 17, 2023).</p>
Synthetic in-situ T/S data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project
<p>"Synthetic observations" of in-situ Temperature and Salinity profiles as a function of depth have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and location (in x,y,z dimensions) where a true in-situ profile exists in the ENACT-4 database (Good et al 2013) over the simulation period: 1980-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal) based on the ISBA-CTRIP hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. The synthetic in-situ T/S dataset from the model is available over the period 1993-2018.</p><p>See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html</p>
Denitrification losses in response to N fertiliser rates - integrating high temporal resolution N2O, in-situ 15N2O and 15N2 measurements and fertiliser 15N recoveries in intensive sugarcane systems
Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.
Black Sea cold intermediate layer cold content from in-situ and modelling sources (1955-2019)
<p>Data files provided in support of 'Warmer winters triggered a reduced and intermittent ventilation regime in the Black Sea', by Capet et al, 2020.</p> <p><a href="https://zenodo.org/api/files/c55a11f7-b395-4002-b76e-1358792e5ec7/Black_Sea_CIL_Cold_Content_Annual.nc">Black_Sea_CIL_Cold_Content_Annual.nc </a> contains the spatial and annual averages of the Black Sea cold intermediate layer cold content, as defined in the above reference, and derived from multiple in-situ and modelling sources, for the period (1955-2017).</p> <p><a href="https://zenodo.org/api/files/c55a11f7-b395-4002-b76e-1358792e5ec7/Black_Sea_CIL_Cold_Content_Weekly.nc">Black_Sea_CIL_Cold_Content_Weekly.nc </a> contains the spatial and weekly averages of the Black Sea cold intermediate layer cold content, as defined in the above reference, and derived from the GHER3D model, for the period (1981-2017).</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data Accompanying "Fracturing process of concrete under uniaxial and triaxial compression: insights from in-situ x-ray mechanical tests"
<p>These are the datasets analysed in "Fracturing process of concrete under uniaxial and triaxial compression: insights from <em>in-situ</em> x-ray mechanical tests" by Stamati <em>et. al.</em> (submitted on June 2020 in Cement and Concrete Research)</p> <p>The datasets contain the reconstructed x-ray 3D images of selected tests and the corresponding DVC analysis.</p> <p>Note that details regarding the DVC analysis can be found in the paper.</p> <ul> <li>Each folder contains a separate test; uniaxial compression ("<em>C-02"</em>) and triaxial compression at 5MPa ("<em>TX5-01"</em>), 10MPa ("T<em>X10-01</em>") and 15MPa ("<em>TX15-01</em>") confining pressures.</li> <li>For each test, folder 01 contains the first scan, where 01 is the reference state for the DVC analyis. Folder 01 also contains the image of the labelled largest aggregates used for the discrete DVC. Folrders 02-XX contain the scans at intermediate loading steps and the corresponding DVC analysis; "<em>reg</em>" contains the registration result in downscaled 2-binning images. "<em>ddic</em>" contains the discrete DVC computation. "<em>ldic</em>" contains the local DVC computation, before and after the merge and filtering of the grid and discrete DVC fields. "<em>strains</em>" contains the strain fields coming from the corrected merged DVC field.</li> <li>Folder "<em>stressStrain</em>" contains the stress-strain curves measured during the tests after subtracting the displacement corresponding to the loading system.</li> <li>Folder "<em>doubleScanUniaxial</em>" contains the two reconstructed images of the "repeated scan" of the uniaxial tests, from which the DVC measurement uncertainties were evaluated.</li> </ul>
In-situ high temperature FTIR, Raman data and breakdown temperature for phlogopite
<p>This dataset contains all new data corresponding to figures in the manuscript, including in situ high temperature FTIR, Raman data, and breakdown temperature from previous studies and this study.</p>
Affinis Snyders Arrowhead (in-situ 3D scan!)
This is my first attempt at 3D scanning an artifact as found, using 29 photos (overhead grid pattern) in 3df zephyr free. It was right before sunset, so the lighting wasn't great, and that made the flaking pattern really hard to see in the model. However, the side that was facing down has some really impressive flaking! The right side is partially missing, as is the better part of the left ear... that makes it really hard to identify. The closest type I could find is Affinis Snyders. The material is a white flint, spattered with iron deposits. Oh... and in case you were wondering what the green stuff is, those were soybean plants. turns out zephyr couldn't do much with them (I'm not a great photographer), so it just pasted them over everything else. Collection id: p68. Source: Objaverse 1.0 / Sketchfab
Real time, in-situ deuteriding of uranium encapsulated in grout; effects of temperature on the uranium-deuterium reaction
<p>To accurately predict the initiation and evolution of uranium hydride potentially present in nuclear waste containers, studies of simulated conditions are required. Here, for the first time, the uranium-deuterium reaction was examined in-situ, in real time, whilst within grouted media. A deuterium gas control rig and stainless steel-quartz glass reaction cell were configured on a synchrotron beam line to collect X-ray diffraction and X-ray tomography data. It was found that deuteride formation, and thus hydride formation, was limited by the uranium and grout thermal conductivities and deuteride initiation only commenced above a threshold temperature. Strong adherence between uranium oxide and grout was also observed.</p>
Northern Hemisphere historical in-situ Snow Water Equivalent dataset (NorSWE, 1979-2021)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Northern Hemisphere historical in situ snow water equivalent dataset (NorSWE) includes snow water equivalent (SWE, or water equivalent of snow cover by WMO, 2018) observations from manual snow surveys, snow pillows, automated passive gamma radiation sensors (GMON), and from airborne passive gamma radiation surveys for the period 1979-2021 compiled from nine different sources covering North America, Russia, Finland, Norway and Switzerland. Exceptionally, to expand coverage over Europe we also include single point manual SWE observations (type_mes=1) from eleven sites in Switzerland. SWE is the primary variable of interest. Snow depth (SD) is included when available and derived bulk snow density is calculated from SD and SWE. NorSWE is described in detail in Mortimer and Vionnet (in prep). Sites intersecting the Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) with a 25 km buffer or a 2° slope mask derived from the GETASSE30 DEM are assigned a mountain mask flag of 1.</p> <p>Processing and quality control generally follows that described in Vionnet et al. (2021). Quality control involved range thresholding: ranges for SD, SWE and bulk density are 0-3 m (0-8 m where mmask = 1), 0-3000 kg m-2 (0-8000 kg m-2 where mmask =1), and 25-700 kg m-3. Where station elevation was not included in the original station metadata or it was deemed to be erroneous, elevation was taken from the United States Geological Survey’s National Elevation Dataset (Gesh et al., 2022). NorSWE was originally compiled to support evaluation of gridded SWE products over the modern satellite era (1979-2021) and focused on observations from snow course and airborne gamma SWE. In v2, we expanded the dataset to include automated data over North America to support hydrological modelling applications. In v3, we added data from Norway and Switzerland (Marty, 2020).</p> <p>NorSWE is provided as a netCDF (NorSWE-NorEEN_1979-2021_v3.nc, compressed into zip file) and following the conventions of the Canadian Snow Water Equivalent Dataset (CanSWE) described in Vionnet et al. (2021) with the addition of a mountain mask variable. The final dataset includes 10 153 locations spanning the years 1979 to 2021.</p> <p> </p> <p><strong>Description</strong> (Francais)</p> <p>Cet ensemble de données de l’Équivalent en Eau de la couverture Neigeuse (EEN, OMM, 2018) comprend des observations manuelles des lignes de neige, des mesures automatiques des coussins à neige et des capteurs gamma passif (GMON), et des estimations de l’EEN issues de mesures de radiation gamma aéroportée pour la période 1979-2021. Cette base de données compile des données issues de neuf sources couvrant l’Amérique du Nord, la Russie et la Finlande. L’EEN est la quantité d’intérêt principal. L’information sur la hauteur de neige (HN) est incluse lorsqu’elle est disponible et la masse volumique moyenne du manteau neigeux est calculée à partir de l’HN et de l’EEN. NorEEN est décrit en détail dans Mortimer and Vionnet (en préparation). Les sites montagneux sont indiqués par le code mmask (valeur = 1). Le masque de montagne combine le Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) (plus une zone tampon de 25 km) et un masque de topographie complexe (2°) calculée selon le modèle numérique de terrain (MNT) GETASSE30.</p> <p>Le traitement des données et le contrôle de qualité (CQ) suivent la méthodologie proposée par Vionnet et al. (2021). Pour le CQ, les observations en dehors de plages de valeurs prédéterminées ont été exclues : HN 0-3 m (0-8 m mmask = 1), EEN 0-3000 kg m-2 (0-8000 kg m-2 mmask = 1), et masse volumique moyenne du manteau neigeux 25-700 kg m-3. Si l’altitude de la station manquait ou était erronée, l’altitude du site a été extraite du fichier national d’élévation de la Commission Géologique des USA (Gesh et al., 2022). Originalement, la base de données décrite dans ce document a été mise en place pour évaluer de produits d’EEN sur grille d’échelle moyennes à large (4-50 km) couvrant la période moderne de la télédétection satellitaire (1979-2021). Pour cette raison, seules les observations des lignes de neige et les estimations de EEN dérivées de mesures de radiation gamma aéroportées avaient été incluses dans la version 1. Pour la version 2, les données historiques des stations automatiques couvrants l’Amérique du Nord ont été incluses en support des applications hydrologiques. Pour la version 3, les données historiques des stations de stations en Norvège et la Swisse ont été incluses (Marty, 2020).</p> <p>La base de données est distribuée au format NetCDF (NorSWE-NorEEN_1979-2021_v3.nc, comprimée dans une archive zip) selon les conventions de la base de données historiques canadiennes d’Équivalent en Eau de la Neige (CanEEN, Vionnet et al., 2021) et comprenant une variable supplémentaire indiquant les sites montagneux. La base de données inclut des mesures issues de 10,153 sites uniques durant la période de 1979 à 2021.</p> <p><strong>References/Références</strong></p> <p>Beaudette, D., Skovlin, J., Roecker, S., and Brown, A.: soilDB: Soil Database Interface. R package version 2.8.5, [codebase] https://CRAN.R-project.org/package=soilDB, 2024.</p> <p>Carroll, T.R. Airborne Gamma Radiation Snow Survey Program: A user's guide, Version 5.0. National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, 2001. https://www.nohrsc.noaa.gov/special/tom/gamma50.pdf</p> <p>Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The National Elevation Dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.</p> <p>Marty, C.: GCOS SWE data from 11 stations in Switzerland, EnviDat, [data Set], https://www.doi.org/10.16904/15, last updated 2024 (last access: February 2025), 2020.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: A hierarchical inventory of the world’s mountains for global comparative mountain science, Sci. Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2 [data set], GMBA-EarthEnv., https://doi.org/10.48601/earthenv-t9k2-1407, 2022, accessed June 2023.</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>Vionnet, V., Mortimer C., Brady, M., Arnal, L., and Brown R.: Canadian historical Snow Water Equivalent dataset (CanSWE 1928-2022), Version 5, Zenodo, https://zenodo.org/records/7734616 , 2021, updated 13 March 2023.</p> <p>WMO (Ed.): Guide to instruments and methods of observation: Volume II - Measurement of Cryospheric Variables, 2018th ed., World Meteorological Organization, Geneva, WMO-No. 8, 52 pp., 2018. <strong><br></strong></p>
Data from: A pioneering experimental investigation of a novel in-situ dynamic characterization of the tensile/compression stress-strain mechanism on human plantar soft tissue
<p><span>We have conducted the first in-situ and in-vivo dynamic mechanical test on human plantar soft tissue. A dynamic mechanical analysis (DMA)-like device has been invented to perform the in-situ and in-vivo stress-strain tests on living plantar in order to characterize the material mechanism of biological soft tissue, whereas it is nearly impossible to prepare a sample from a living body for classical tests. A series of pioneering tests of tensile/compression on the heel of ten volunteers are reported, with the reference of tests on mimic foot model made by silicon rubber, standard silicon rubber brick sample, and finite elementary analysis. In addition to demonstrating the effectiveness of the device and approach, interesting correlations between the results and clinic data were found, suggesting considerable potential for the invention in future research.</span></p>
In-situ Liaodong Bay Sea Ice Data for the Study of Remotely Estimating Absorptive Substances within Sea Ice
<p>This dataset was used for a study trying to develop remote rensing retrieval model and analyse the uncertainties of impurity content in sea ice from the optics perspective.</p>
Datasets for airborne in-situ quantification of methane emissions from oil and gas production in Romania
<p>This dataset includes airborne in-situ measurements taken around target clusters and regions of oil and gas production sites in Romania, as well as two models outputs interpolated to the flight tracks during the ROMEO (ROmanian Methane Emissions from Oil and gas) campaign that took place in Romania in 2019. The dataset is used for the evaluation presented in the manuscript titled: "Airborne in-situ quantification of methane emissions from oil and gas production in Romania."</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
In-situ feeding as a new management tool to conserve orphaned Eurasian lynx (Lynx lynx)
High human-caused mortality due to wildlife-vehicle-collisions and illegal killing leads to frequent cases of orphaned Eurasian lynx juveniles. Under natural conditions, this would result in starvation of the young. To avoid this, wildlife managers conventionally rear animals in captivity and release them later. However, this measure is an undesirable outcome for species conservation, managers and animals alike. Increased awareness of Eurasian lynx orphaned by human-caused mortality means managers must often intervene in endangered populations. In this study we report for the first time a successful case of in-situ feeding designed to avoid captivity of two orphaned Eurasian lynx. We exposed 13 roe deer and 7 red deer carcasses in the field to successfully support two orphans to the age of independence and confirm dispersal from the natal range. We present this management approach as a feasible and complimentary tool that can be considered in small or isolated large carnivore populations where every individual counts towards population viability.
Measurement report: Vertical profiling of particle size distributions over Lhasa, Tibet: Tethered balloon-based in-situ measurements and source apportionment
<p>Particle size distribution data in summer 2020 in Lhasa, Tibet for https://doi.org/10.5194/acp-2021-810</p>
Pore matrix dissolution in carbonates: An in-situ experimental investigation of carbonated water injection
<p>Carbonate rocks in underground formations are major targets for oil extraction and carbon storage. The solid part of these porous rocks contains certain minerals, such as calcite and dolomite, that can interact with aqueous solutions. Interactions could be reactive, which leads to the dissolution of these minerals. In this study, we investigated the evolution of carbonate rock dissolution during the flow of carbonated water in pores that initially contain both oil and brine. Carbonated water is an aqueous solution enriched with carbon dioxide (CO<sub>2</sub>); hence, it is acidic. In our experiments, we observed that the reactive flow and transport of carbonated water is characterized by two distinct periods. The first is a pre-dissolution period where the CO<sub>2</sub> molecules diffused from the flowing carbonated water into the oil causing it to swell. As separate oil globules swelled, they reconnected and moved in the direction of the flowing water toward the outlet of the rock sample. In the second stage, significant mineral dissolution occurred creating wormholes that had either a conical or a dominant pattern. The pattern and extent of dissolution was dependent on the flow rate of the carbonated water and its CO<sub>2</sub> concentration.</p>
Localizing Hydrological Drought Early Warning using In-Situ Groundwater Sensors - Veness et al. (2022) - Dataset
<p>This upload contains full input data and modelling scripts to support AGU WRR's 'Localizing Hydrological Drought Early Warning using In-situ Groundwater Sensors' (Veness et al., 2022).</p> <p>'WRR_Data_Extraction' contains input data and processing of these for model input.</p> <p>'WRR_Modelling_Methods' contains two folders. 'Groundwater Model & Calibration' provides the code for the modified AquiMod, formatted for input in to an automated calibration procedure (run time approx. 5 minutes with 10,000 runs). The 'Plotting Files' folder contains the code converting the groundwater levels from the automated calibration procedure to plots seen in the paper.</p> <p>These scripts require use of the data and functions within the 'data_and_functions' folder, which may require careful directory management and minor edits to the code to ensure the scripts can communicate.</p>
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations
<p>The various data used for the paper "A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model: Its parameterization, implementation, and evaluation" submitted to Geoscientific Model Development for publication are shared here.</p>
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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.