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138 results for “dynamic mapping”

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zenodo48/100

Land Productivity Dynamics (LPD) maps based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region (BSR).

Maps of the land productivity dynamic (LPD) based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region for the period 2001-2015. LPD from Trends.Earth (TE), LPD from the Joint Research Center (JRC-LPD), and LPD from the Food and Agriculture Organization – World Overview of Conservation Approaches and Technologies (FAO-WOCAT LPD). In addition, TE-based LPD with climate correction based on Rain Use Efficiency (RUE), Residual Trend Analysis (RESTREND), and Water Use Efficiency (WUE) calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution for 2001 and 2015, and the 16-day MOD13Q1 NDVI dataset from 2001 to 2015 were used as inputs. ********************** A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the LPDs. Value  Meaning -32768 No data 1           Declining 2          Moderate decline 3          Stressed 4          Stable 5          Increasing Funding: this study was undertaken as part of the Satellite Desertification Monitoring Program in the Brazilian Semiarid Region [Grant Number 403223/2021-0] supported by the CNPq. It also had the support of Capes, through Notice no. 28/2022 – PDPG Social Vulnerability & Human Rights [Grant Number 88881.705050/2022-01].

opencc-by-4.0Apr 2023View details →

edi48/100

Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA

This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.

openCC (other)May 2023View details →

edi48/100

Tree Map for Census at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico

This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Princi

openCC (other)Nov 2023View details →

zenodo44/100

Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset

Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames 1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points 2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin 3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin The relevant code is available at:  OGM prediction: https://github.com/TempleRAIL/SOGMP OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch

opencc-by-4.0Sep 2022View details →

zenodo44/100

The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism

This dataset contains the following figures: Table 1, The Radar Chart Map 1, ROME, 2nd CENTURY BCE Map 2, ROME, 1st CENTURY BCE Map 3, ROME, 1st CENTURY ACE Map 4, ROME, 2nd CENTURY ACE Map 5, ROME, 3rd CENTURY ACE

opencc-by-4.0Mar 2023View details →

zenodo40/100

Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"

Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves.  <h2> </h2> <h2>Interactive Figure Data</h2> Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII). clouds_{line_name}.csv: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight. transfer_function_velocity_{line_name}.csv: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity. transfer_function_tau_{line_name}.csv: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days. transfer_function_{line_name}.csv: A CSV file containing the transfer function Ψ.   <h2>Model-Related Figures</h2> fitplot_low.pdf: Same as Figure 4 in the publication, but for the low state. fitplot_high.pdf: Same as Figure 4 in the publication, but for the high state. geoplot_low.pdf: Same as Figure 5 in the publication, but for the low state. geoplot_high.pdf: Same as Figure 5 in the publication, but for the high state. lagplot_low.pdf: Same as Figure 6 in the publication, but for the low state. lagplot_high.pdf: Same as Figure 6 in the publication, but for the high state.    <h2>Spectral Reduction Method Comparison</h2> spec_decomp_pyqsofit.pdf: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels. input_method_comp.pdf: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.   <h2>Published Value Comparison</h2> pubval_table.pdf: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024).    <h2>Joint Posterior Analysis</h2> joint_line_posterior_table.pdf: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species.    <h2>Virial Factor Analysis</h2> fcomp.pdf: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states. fcorr_table.pdf: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.

opencc-by-4.0Aug 2024View details →

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Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'

This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'. Metrics calculated for each of the 4269 50 km by 50 km regions <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> ifpa_count.nc ifpa_count_max_20.nc ifpa_count_max_100.nc ifpa_count_max_250.nc </td> <td>bedmach_count.nc bedmach_count_max_20.nc bedmach_count_max_100.nc bedmach_count_max_250.nc</td> <td> </td> <td> The number of hills with a 50 m prominence within a 5 km neighbourhood  (or 20 m, 100 m, 250 m respectively) </td> </tr> <tr> <td>ifpa_b1_5km.nc ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc i_std_l.nc</td> <td>bedmach_std_deslope.nc b_std_l.nc</td> <td> </td> <td>The standard deviation: - with the best fit slope removed - of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of: - the bed elevation - some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table>   Datasets required for plotting <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line  </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> IFPA_bed_C50.nc </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> To plot the figures, you will either require the following datasets:  <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> HA_data.csv HB_data.csv RSB_data.csv </td> <td>For Highland A, Highland B and Recovery Subglacial Basin IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> HA_data_ifpa.csv HB_data_ifpa.csv RSB_data_ifpa.csv </td> <td> For Highland A, Highland B and Recovery Subglacial Basin IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile </td> </tr> </tbody> </table> or, the figures can be regenerated using the following datasets, which are available at the listed DOIs <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table>

openmit-licenseMay 2024View details →

zenodo40/100

Underlying data for IsoAligner: dynamic mapping of amino acidpositions across protein isoforms

The human isoform library (list_of_gene_objects_25th_july_final.txt) for the IsoAligner webtool is generated from these resources.

opencc-by-4.0Feb 2022View details →

zenodo40/100

Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex

Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River

opencc-by-4.0Dec 2020View details →

zenodo40/100

Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve

Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)

opencc-by-4.0Dec 2023View details →

zenodo40/100

SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling

SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth.  This package includes a Technical Documentation with the details about the use of the data product.

opencc-by-4.0Dec 2020View details →

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Supplementary data for "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"

Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"

opencc-by-4.0Jun 2022View details →

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Dataset of "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells"

This dataset supports the article: "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells"   Raw data for the article "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells". For further details see the readme.txt file.

opencc-by-4.0Nov 2024View details →

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Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors

MD simulation data of compound 1 in MSM 2-S3 conformations of the related to the publication Pantsar et al.: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors. Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files). All datasets related to this publication: <a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound 1; dataset: I) <a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound  1; dataset: II) <a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound  2; dataset: III) <a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound  2; dataset: IV) <a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (SB203580; dataset: V) <a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (SB203580; dataset: VI) <a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound 1 simulated in compound 2 metastable state 2-S3; dataset: VII) <a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound 1 simulated in compound 2 metastable state 2-S3; dataset: VIII) <a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: IX) <a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: X) <a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: XI) The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).

opencc-by-4.0Feb 2021View details →

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Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors

opencc-by-4.0Oct 2021View details →

zenodo36/100

Dataset (V) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors

MD simulation data of SB203580 related to the publication Pantsar et al.: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors. Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files). All datasets related to this publication: <a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound 1; dataset: I) <a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound  1; dataset: II) <a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound  2; dataset: III) <a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound  2; dataset: IV) <a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (SB203580; dataset: V) <a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (SB203580; dataset: VI) <a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound 1 simulated in compound 2 metastable state 2-S3; dataset: VII) <a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound 1 simulated in compound 2 metastable state 2-S3; dataset: VIII) <a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: IX) <a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: X) <a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds 1 and 2; dataset: XI) The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).

opencc-by-4.0Feb 2021View details →

dryad36/100

Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter

Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of Deinococcus radiodurans Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.

opencc-zeroNov 2022View details →

zenodo36/100

Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"

The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making. All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow. The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a> The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a> To obtain maximum compression of the data, the tar command used to generate this tarball was: <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre>

opencc-by-4.0Mar 2023View details →

zenodo36/100

Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2.  The data is as follows: scATAC: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample. scRNA: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`).  multiome: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes.

opencc-by-4.0Aug 2023View details →

ClinicalTrials.gov36/100

Ablation Targets of Scar-related Ventricular Tachycardia Identified by Dynamic Functional Substrate Mapping

ClinicalTrials.gov study NCT05086510. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record