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12,346 results for “derivatives”
CLDF dataset derived from Ratcliffes's "Glottometrics of Arabic" from 2021
<p>Cite the source of the dataset as:</p> <blockquote> <p>Ratcliffe, Robert R. (2021): The glottometrics of Arabic. Language Dynamics and Change. 2021. DOI: 10.1163/22105832-01001100.</p> </blockquote>
Research data for: "Implicit and explicit host effects on excitons in pentacene derivatives"
<p>This file was created by Robert J. Charlton on 26th November 2017. It contains all input files necessary to reproduce the results presented in the publication "Implicit and explicit host effects on excitons in pentacene derivatives". The data in this file is organised as follows.</p> <p> The folder ONETEP contains input files to perform geometry optimisations, natural bond orbital (NBO) analysis, excited state energy calculations with TDDFT & \Delta SCF and decomposition of Kohn-Sham molecular orbitals in terms of the underlying NGWF basis. Also included are compilations of data used for plotting Figures in the main publication and the Supporting Information (SI). Further details can be found in the relevant subfolders.</p> <p>The folder Gaussian contains input and output files for excited state calculations with various exchange-correlation (XC) functionals and solvent environments performed with Gaussian 09. The main XC functionals used are the semi-local PBE and two optimally tuned (OT) range-separated hybrid functionals, OT-LCwPBE and OT-CB3LYP. The file all_rsep_params.txt contains the parameters used for tuning the range-separated functionals; see the SI for more details.</p> <p>Update: Gaussian folder also contains Density of States (DOS) data.</p>
Determining the transfectability of different patient-derived cell lines
<p>Determining whether the following patient-derived cell lines can be transfected efficiently using Lipofectamine 2000</p> <p>C2C12</p> <p>HSJD-DIPG-008</p> <p>HSJD-DIPG-011</p> <p>HSJD-DIPG-012</p> <p>HSJD-DIPG-013</p> <p>SU-DIPG-IV</p> <p>HSJD-GBM-002</p> <p>HSJD-DIPG-007</p> <p> </p>
Aerosol Optical Depth (AOD) over the Nepalese cryosphere derived from an empirical model
<p>In this manuscript, we demonstrate the spatial and temporal variability of AOD over the Nepalese cryosphere may be reconstructed through empirical relationships developed from numerical weather model analysis fields and geographic variables. Our manuscript is novel, providing:<br> * a capability to extrapolate AOD over the cryospheric portion of Nepalese Himalayas<br> * an understanding of the relationships between ECMWF Reanalysis with observed AOD<br> * and a cross-validation of an empirical model using MODIS derived AOD and AERONET data.</p> <p>The empirical model that we present in this study, could potentially be applied to other mountains regions. We believe that the findings presented in our manuscript provide a novel approach to address the challenge of paucity of observations for aerosols in the cryospheric portion in Himalaya region. </p> <p> </p> <p> </p>
Derived brain morphology measures from CamCAN data
<p>Derived brain morphology measures from CamCAN data.</p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Optimisation of viability assay for DIPG patient-derived cell lines
<p>Evaluation of the efficacy of M4K compounds in DIPG patient-derived cell lines is essential before any promising compounds can be further tested in mouse xenograft models. This approach can aid in narrowing down clinical compound candidates and reduce the time, resources and animal sacrifice needed downstream.</p> <p>A robust and efficient readout for the changes in the viability of the DIPG cells needs to be established before it can be used to evaluate the M4K compounds. In addition, the amount of cells to be seeded at the beginning of the experiment has to be optimised to avoid overcrowding and starvation of the cells after extended culture times. Overcrowding and starvation will lead to increased cell death and prevent accurate estimation of the potency of M4K compounds (EC50).</p>
Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014
<b>Description: </b><p>The work was carried out in the oil palm plantations within the Stability of Altered Forest Ecosystem (SAFE) Project, located within lowland dipterocarp forest regions of East Sabah in Malaysian Borneo. Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0°, and a footprint of about 40 cm diameter. The average pulse density was 7.3/m2. The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/25"><b>Influences of disturbance and environmental variation on biomass change in Malaysian Borneo</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, JKM/MBS.1000-2/2 JLD.3 (128))</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.3 (128))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247699">here</a></p><p><b>Files: </b>This consists of 1 file: LiDAR_Aboveground_Carbon.xlsx</p><p><b>LiDAR_Aboveground_Carbon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>LiDAR aboveground carbon in Oil palm plantations</b> (described in worksheet LiDAR_ Aboveground_Carbon)</p><p>Description: The output of a LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data was pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </p><p>Number of fields: 35</p><p>Number of data rows: 27</p><p>Fields: </p><ul><li><b>Year</b>: Year the oil palm trees were planted (Field type: Numeric)</li><li><b>Plot</b>: Plot number based on the SAFE project framework. Each plot is 25 metres x 25 metres size or 0.0625 hectares (Field type: Location)</li><li><b>meanH</b>: Average tree height per plot (Field type: Numeric)</li><li><b>TreeN_plot</b>: Number of trees per plot (Field type: Numeric)</li><li><b>TreeN_ha</b>: Number of trees per hectare obtained by upscaling the number of trees within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>ACD_plot</b>: Sum of the aboveground carbon density per plot (Field type: Numeric)</li><li><b>ACD_ha</b>: Sum of the aboveground carbon density per hectare obtained by upscaling the number of aboveground carbon density within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>CC1</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 1 metre height (Field type: Numeric)</li><li><b>CC2</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 2 metres height (Field type: Numeric)</li><li><b>CC3</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 3 metres height (Field type: Numeric)</li><li><b>CC4</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 4 metres height (Field type: Numeric)</li><li><b>CC5</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 5 metres height (Field type: Numeric)</li><li><b>CC6</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 6 metres height (Field type: Numeric)</li><li><b>CC7</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 7 metres height (Field type: Numeric)</li><li><b>CC8</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 8 metres height (Field type: Numeric)</li><li><b>CC9</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 9 metres height (Field type: Numeric)</li><li><b>CC10</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 10 metres height (Field type: Numeric)</li><li><b>CC11</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 11 metres height (Field type: Numeric)</li><li><b>CC12</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 12 metres height (Field type: Numeric)</li><li><b>CC13</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 13 metres height (Field type: Numeric)</li><li><b>CC14</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 14 metres height (Field type: Numeric)</li><li><b>CC15</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 15 metres height (Field type: Numeric)</li><li><b>CC16</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 16 metres height (Field type: Numeric)</li><li><b>CC17</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 17 metres height (Field type: Numeric)</li><li><b>CC18</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 18 metres height (Field type: Numeric)</li><li><b>CC19</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 19 metres height (Field type: Numeric)</li><li><b>CC20</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 20 metres height (Field type: Numeric)</li><li><b>CC21</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 21 metres height (Field type: Numeric)</li><li><b>CC22</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 22 metres height (Field type: Numeric)</li><li><b>CC23</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 23 metres height (Field type: Numeric)</li><li><b>TCH</b>: Top of canopy height: mean height of Canopy Height Model (CHM) pixels per hectare. (Field type: Numeric)</li><li><b>TreeN_itc</b>: Number of segmented trees per hectare obtained by using the itcSegment function implemented in R (Field type: Numeric)</li><li><b>meanH_itc</b>: Average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>meanHc_itc</b>: Corrected average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>ACDc_itc</b>: Sum of the aboveground carbon density per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2014-11-05 to 2014-11-05</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Different methods to evaluate the effects of M4K compounds on DIPG patient-derived cell lines
<p>Aside from determining the optimal numbers of DIPG cells to be seeded for viability assay, it is also important to validate that measurements using CellTiter Glo are in agreement with other methods of determining cell viability.</p> <p>For my blog post about this experiment, please visit my opennotebook page.</p> <p>https://openlabnotebooks.org/different-methods-to-evaluate-the-effects-of-m4k-compounds-on-dipg-patient-derived-cell-lines/</p>
InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives"
<p>InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives", Punjab University Journal of Mathematics, October 2019. Download latest release of InCLosure via <a href="https://doi.org/10.5281/zenodo.2702404">https://doi.org/10.5281/zenodo.2702404</a></p>
Scatter plots as per Fig. 4, and Spectra-derived TiO2 as per Fig. 7
<p>F. Zhang, J. W. Head, C. Wöhler, R. Bugiolacchi, L. Wilson, A. T. Basilevsky, A. Grumpe, Y. L. Zou.</p> <p>Ring-Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin.</p> <p>Journal of Geophysical Research: Planets, 125, e2019JE005967. https://doi.org/10.1029/2019JE005967</p> <p> </p> <p><strong>Figure 4.</strong> Scatter plots of diameters, heights, and volumes of the 532 RMDSs measured from NAC-derived DTMs: (a) diameter vs. height; (b) diameter vs. volume</p> <p><strong>Figure 7. </strong>M<sup>3</sup>-spectra-derived TiO<sub>2</sub> contents of RMDSs and global maria (latitude range +/- 75 degrees).<strong> </strong>(a) Mean TiO<sub>2</sub> wt% vs. diameter for 2407 RMDSs in seven large mare regions indicated in Figure 2; (b) The TiO<sub>2</sub> content histogram of the measured 2407 RMDSs (green color), compared with the global TiO<sub>2</sub> distribution of all lunar mare regions (red color). Both histograms are normalized to 1, such that the sum of all red or green histogram bins is 1, respectively. The global Ti abundance was taken from the complete lunar mare surface at a latitude range +/- 75 degrees</p> <p> </p>
Evaluating the effect of 8 M4K compounds on the viability of DIPG patient-derived cells (SU-DIPG-VI, HSJD-DIPG-007 and SU-DIPG-IV)
<p>Viability assay using Celltiter Glo kit has been chosen as the main method for evaluating the efficacy of ALK2 inhibitors on DIPG patient-derived cells due to its robustness. As a pilot experiment, 8 ALK2 inhibitors with differing potencies were chosen and evaluated in 3 DIPG patient-derived cell lines (one with wild-type ALK2 and two with mutant ALK2).</p>
Earthflow-Derived Boulder Size Distributions for Arroyo Hondo and the Eel River
<p>These are measured size distributions (in meters) for boulders that are supplied from earthflows in California's Franciscan Melange where they intersect channels at Oak Ridge Earthflow along Arroyo Hondo, in Central California and at the Mile 201 Slide along the Eel River, in Northern California. The dimensions reflect the minor axis length of boulders as measured from Google Earth imagery. The measurement threshold for boulders in this imagery is about 30 cm.</p>
Fibroblast-derived PI16 as a novel regulator of neuropathic pain
<p>Non-neuronal cells, including glia and leukocytes contribute to chronic pain. Here we identify fibroblasts secreting the protein PI16 as novel regulator of neuropathic pain. Mice deficient in PI16, a member of the CAP superfamily of proteases, are protected against neuropathic pain induced by spared nerve injury (SNI) or paclitaxel. SNI increases PI16 in fibroblasts and in vitro myofiborblast differentiation increases PI16 expression and secretion. In vitro, PI16 conditioned medium promotes migration of immune cells across the endothelial layer. Consistently, protection against neuropathic pain in PI16-/- mice was associated with reduced endothelial barrier permeability, lower leukocyte infiltration and reduced activation of the endothelial barrier regulator MLCK. Collectively our findings indicate that the fibroblast- derived protein PI16 promotes neuropathic pain by increasing vascular permeability and cellular infiltration. Due to its key role in pain and limited cellular distribution PI16 is an attractive novel target for pain management.</p>
Derivatives Law and Practice in the Middle East
<p>The purpose of the paper, part of a project by Boston University Law School, is to examine derivatives law in some Middle Eastern countries including the UAE.</p>
Keratinocytes derived from patient-specific iPSCs recapitulate the genetic signature of psoriasis disease
<p>Induced pluripotent stem cells (iPSCs) were generated from control (Ctrl) and two psoriasis patients (PsO1 and PsO2) having genetic background of psoriasis. The iPSCs were differentiated into epidermal keratinocytes as described in the article entitled "Keratinocytes derived from patient-specific iPSCs recapitulate the genetic signature of psoriasis disease" by Gowher et al. The dataset represent RNA-seq data generated from keratiocytes derived from ctrl-iPSCs and psoriasis patients iPSCs (PsO1 and PsO2).</p> <p>The file name is : Sample name with replicate_overall sample number_read direction_001 where:</p> <p>- Ctrl Rep1 and Ctrl Rep2: Keratinocytes derived from control (Ctrl-iPSCs). </p> <p>- PSO1 Rep1 and PSO1 Rep2: Keratinocytes derived from psoriasis patient iPSCs (PSO1-iPSCs)</p> <p>- PSO2 Rep1 and PSO2 Rep2: Keratinocytes derived from psoriasis patient iPSCs (PSO2-iPSCs).</p> <p>- RNA-seq data was generated from two biological repliicates (Rep1 and Rep2).</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p> <p> </p>
Stream network for the SAFE Project derived from SRTM data
<b>Description: </b><p>This zipfile contains linear geometries showing a channel network across the SAFE Project landscape. The network was calculated using the GRASS hydrology tool <code>r.stream.extract</code> from SRTM elevation data and the resulting flow accumulation predictions (see <a href="https://zenodo.org/record/3490488">https://zenodo.org/record/3490488</a> and <a href="https://zenodo.org/record/3490687">https://zenodo.org/record/3490687</a>).<br><br>Note that these networks are derived entirely from remotely sensed data, but do form a single interconnected network across the wider SAFE landscape. Other stream network data are available () but the provenance of these are not well known and they only cover part of the SAFE network.<br><br>Details of the geoprocessing can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/stream_networks">https://www.safeproject.net/dokuwiki/safe_gis/stream_networks</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492009">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_SRTM_Stream_network_metadata.xlsx, SRTM_Channels_network.zip</p><p><b>SAFE_SRTM_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Channels_network.zip</b></p><p>Description: Shapefile containing 54413 calculated segments forming a channel network across the wider SAFE landscape.</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet Properties)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 17</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>cat</b>: Identity of segment (Field type: id)</li><li><b>type_cd</b>: Segment type (see also type) (Field type: numeric)</li><li><b>ID</b>: Identity of segment (Field type: id)</li><li><b>length</b>: Length of network segment (Field type: numeric)</li><li><b>n_ponts</b>: Number of points in segment (Field type: numeric)</li><li><b>sourceX</b>: X coordinate of source point of segment (Field type: numeric)</li><li><b>sourceY</b>: Y coordinate of source point of segment (Field type: numeric)</li><li><b>sinkX</b>: X coordinate of sink point of segment (Field type: numeric)</li><li><b>sinkY</b>: Y coordinate of sink point of segment (Field type: numeric)</li><li><b>type</b>: Segment type - is the segment a source or outflow segment (connected at one end only, type_cd = 0) or internal (connected at both ends, type_cd=1) (Field type: categorical)</li><li><b>snkChnn</b>: Identity of sink channel for this segment (Field type: id)</li><li><b>srcChnn</b>: Identities of source channels for this segment (Field type: id)</li><li><b>nSourcs</b>: Count of source channels (Field type: numeric)</li><li><b>sorcElv</b>: Elevation of start point of segment (Field type: numeric)</li><li><b>sinkElv</b>: Elevation of sink point of segment (Field type: numeric)</li><li><b>sorcFlw</b>: Incoming flow at segment source (Field type: numeric)</li><li><b>sinkFlw</b>: Outgoing flow at segment sink (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Antarctic Snow Radar-Derived Relative Accumulation Product
<p>Small-scale Antarctic accumulation rates relative to MERRA-2 precipitation-evaporation as derived from NASA Operation IceBridge snow radar. </p>
Supplementary data for the manuscript "Thermodynamic properties of isoprene and monoterpene derived organosulfates estimated with COSMOtherm"
<p>.cosmo and .energy files of various isoprene and monoterpene derived organosulfates and methyl bisulfate (neutral and deprotonated), IEPOX (neutral) and hydrated sodium (cation).</p>
Antimicrobial molecules derived from kefir, effective against P aeruginosa and MRSA strains
<p>These files contain the NGS data of the partial 16.S rRNA gene (V3-V4 region) amplicon from Kefir grains used within the manuscript "Antimicrobial molecules derived from kefir, effective against P aeruginosa and MRSA strains"</p>
ScienceDex guides
Understand access before you commit
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.