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Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
Soil Characteristics Following a Lightning-Initiated Fire at MacKenzie Flats, Sevilleta National Wildlife Refuge, New Mexico (1998)
We evaluated soil characteristics after a lightning-initiated fire. Following the fire in July 1998, 25 experimental plots were established on the eastern edge of MacKenzie Flats at the Sevilleta National Wildlife Refuge. Ten of these plots were located in a Bouteloua gracilis (blue grama)-dominated site, while 15 were established in another area dominated by Bouteloua eriopoda (black grama). All plots were oriented along a topographic gradient that ran in an east-west direction. At three topographic locations within each plot, soil samples were taken at two depths from an area covered by perennial grass as well as an area devoid of vegetation. Soil samples were collected in July 1998 and analyzed for moisture content and soil texture.
Data set for initializing and forcing of high-resolution local area model implementations in two ATLAS case study areas, Rockall Bank and Condor Seamount
<p>The data set includes all necessary data for set up, initial and boundary conditions of high-resolution local area model implementations using the ROMS-AGRIF model in two case study areas, Rockall Bank and Condor Seamount, conducted as part of the EU ATLAS project. The data include all computational grids, initialization fields (temperature, salinity) and boundary conditions (temperature salinity, currents, sea surface height) for each case study area.</p>
Winter snow depths for initializing a glacio-hydrological model in high mountain Chile
<p>The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (<em>Finger et al., 2011; Ragettli and Pellicciotti, 2012</em>) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - <em>Burger et al., 2018</em>). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020. </p> <p> </p> <p>Data and file repository for the submitted article:<br> %-------------------------------------------------------------<br> %-------------------------------------------------------------</p> <p> On the utility of optical satellite winter snow depths for modelling the<br> glacio-hydrological behaviour of a high elevation, Andean catchment.</p> <p>Thomas E. Shaw1, Alexis Caro1,2, Pablo Mendoza3, Álvaro Ayala4, Francesca Pellicciotti5,6, Simon Gascoin7, James McPhee1,3</p> <p>1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile<br> 2 Univ. Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France<br> 3 Department of Civil Engineering, Universidad de Chile, Santiago, Chile<br> 4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile<br> 5 Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland<br> 6 Department of Geography, Northumbria University, Newcastle, UK<br> 7 CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France</p> <p>%-------------------------------------------------------------<br> %-------------------------------------------------------------<br> The following sub-folders are separated into forcings, grids, initial model conditions, model files and parameters.</p> <p>This file describes briefly the contents of each sub-folder.</p> <p>%-------------------------------------------------------------<br> FORCINGS:</p> <p>CloudCover_TPK.csv - A timeseries of hourly cloud cover fraction (-) derived NASA POWER archives.<br> Discharge_TPK.csv - A timeseries of hourly discharge (m3 s-1) from the outlet station F_TdP.<br> Master_Data_TPK.mat - a Matlab structure (written in version 2017a) for all data availble to the catchment for the considered model period.<br> Precipitation_TPK.csv - A timeseries of hourly precipitation (mm/hr) from AWS TdP.<br> Temperature_TPK.csv - A timeseries of hourly temperature (degC) from AWS TdP. <br> TemperatureGradient_TPK.csv - A timeseries of calibrated temperature gradients based upon forcing from AWS TdP.</p> <p>%-------------------------------------------------------------<br> GRIDS:</p> <p>42 ascii files for various grids (primary or secondary) use to derive the .TES file (see TOPKAPI-ETH sub-folder) for running the model.<br> Associated projection (.prj) files are given.<br> Naming convention is provided in the manual (see TOPKAPI-ETH sub-folder) except:<br> rdy_SoilDepth.asc - An adjusted top layer soil depth map based upon Ragettli et al. (2012).<br> rdy_debris_v.asc - A debris thickness map for Piramde Glacier and the tongue of Bello Glacier. Values adjusted slightly from Ayala et al. (2016) to account for areas that are not debris, but bedrock (Bello Glacier).</p> <p>%-------------------------------------------------------------<br> INITIAL_CONDITIONS:</p> <p>Sub-folder 'Albedo': <br> Albedo_Pleiades.asc - An albedo map derived from the model spin up and limited to the snow-covered pixels of the Pléiades snow depth map.<br> Sub-folder 'Snow':<br> XXX_snow_mmwe.asc - A snow water equivalent map (mm w.e.) given by the initialisation method 'XXX' (Pléiades, TOPO or DBSM). TPK is derived solely from the model spin up (an input grid not required). <br> XXXeq_snow_mmew.asc - As above, though considering the equal means approach described in the manuscript. TPK included here.<br> Sub-Folder 'SpinUp_State':<br> 201709040000.stt - The system state file that contains information on the equiblibrium state of catchment (as read by the model upon initialisation). Running the model with a spinup shuld call upon this file within the command prompt.</p> <p><br> %-------------------------------------------------------------<br> PARAMETERS:</p> <p>Sub-folder 'Calibration'<br> TPK_ParameterAllocation - A Matlab script for the establishing the Monte Carlo parameter simulation and running the model n times. The current script is considered for soil parameters.<br> Sub-folder 'Sensitivity'<br> TPK_Sensivity_Analysis - A Matlab script for establishing the upper and lower boundaries of parameter/forcing sensitivities for a one-at-a-time analysis.</p> <p> </p> <p>%-------------------------------------------------------------<br> RESULTS:</p> <p>Model_Output_Comparison.mat - A matlab file with output grids and vectors for model intercomparisons (i.e. Pléiades (Pléiades-Uncertainty and Pléiades+Uncertainty), TOPO, TPK, DBSM + equal means equivalents). Files are:<br> All_S - Daily snow mass balance grids (mm w.e.)<br> Bias_Month - Monthly bias (row) of modelled vs measured streamflow at F_aP site for each model run (column).<br> Date_Daily - Numeric date of daily grids<br> DateTPK - Numeric date of hourly model simulations<br> Gla_Map - Daily cumulative glacier modelled mass balance grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> GMB_Bello - Cumulative modelled mass balance (mm w.e.) of Bello Glacier AWS grid cell<br> GMB_Piramide - Cumulative modelled mass balance (mm w.e.) of Piramide Glacier AWS grid cell<br> GMB_Yeso - Cumulative mass modelled balance (mm w.e.) of Yeso Glacier AWS grid cell<br> KGE_Month - KGE values per month (row) and for each model run (column)<br> M3AP - Measured streamflow at F_aP<br> M3TP - Measured streamflow at F_TdP<br> MeltG_Avg_all - Mean hourly ALL-glacier melt rate (mm w.e./hr) for each model run (column)<br> MeltS_Avg_all - Mean hourly catchment-wide melt rate (mm w.e./hr) for each model run (column)<br> MOD_SnowCC - Daily MODIS snow cover fraction<br> Model_Name - .... well, its the name of the model run :=)<br> MODIS_SLE - The calculated Snow Line Elevation (m a.s.l.) for each daily MODIS scene<br> PlanetSLE - As above, but for PlanetScope images (17 days total)<br> Planet_SnowObs - The numeric dates of the equivalent PlanetSLE data<br> Q_Mod_aP - The modelled hourly streamflow at F_aP<br> Q_Mod_TdP - The modelled hourly streamflow at F_TdP<br> Q_Prc_Month - The percentage difference in monthly (row) modelled-measured streamflow by model run (column)<br> R_Month - Correlation values per month (row) and for each model run (column)<br> RelVar_Month - Relative variance per month (row) and for each model run (column)<br> Snow_Map - Daily snow water equivalent grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation<br> SnowCC - The hourly modelled snow cover fraction for the catchment for each model run (column)<br> TPKSLE - The daily modelled TOPKAPI-ETH model SLE from each model run (column)</p> <p> </p> <p>%-------------------------------------------------------------<br> TOPKAPI-ETH:</p> <p>FIUME - A generic file type that is called by the model to ID the name of the study site. I this case 'rdy'.<br> rdy.TES - A vectorised file of all grids required by the model to run.<br> rdy.TPK - The TPK parameter and command file. This is adjusted to change input parameters and forcing files etc. The current file is the optimised version for this catchment.<br> TManual_Aug2013.pdf - A PDF instruction file (semi-complete) for the model written by Stefan Rimkus (2013). The naming conventions and grid names are given here.</p> <p> </p> <p> </p> <p>CITED MATERIAL REGARDING THE MODEL</p> <p><strong>Burger, F., Ayala, A., Farias, D., Shaw, T. E., Macdonell, S., Brock, B., McPhee, J., Pellicciotti, F. (2018a). Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment: understanding the role of debris cover in glacier hydrology. Hydrological Processes, SI-Latin(January), 1–16. <a href="https://doi.org/10.1002/hyp.13354">https://doi.org/10.1002/hyp.13354</a></strong></p> <p><strong>Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., & Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resources Research, 47(7), 1–14. <a href="https://doi.org/10.1029/2010WR009824">https://doi.org/10.1029/2010WR009824</a></strong></p> <p><strong>Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters. Water Resources Research, 48(3), n/a-n/a. <a href="https://doi.org/10.1029/2011WR010559">https://doi.org/10.1029/2011WR010559</a></strong></p> <p> </p>
Workshop - European initiatives for nutrient recycling in agri-food systems
<p>This is the video of the workshop (16 December 2019 – Vila-sana, Lleida, Spain) jointly organised by the Circular Agronomics project and the H2020 project <a href="https://www.nutri2cycle.eu/">Nutri2Cycle</a> on European initiatives for nutrient recycling in agri-food systems.</p> <p>The event showcased the various strategies that exist across Europe to recycle nutrients and that the different EU-funded projects already working to achieve this can complement one another in order to attain a common goal.</p> <p>One of the experimental sites within the Catalonia case study of the Circular Agronomics project was visited.</p>
SIRAH-CoV2 initiative: NSP15 Endonuclease (PDB id:6W01)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Endoribonuclease NSP15 in its APO form (PDB id: 6W01, Bioassembly 1). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W01_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W01_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W01_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W01_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W01_SIRAHcg_prot.prmtop 6W01_cg_prot.ncrst 6W01_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Exequiel Barrera (ebarrera@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Apo ADP ribose phosphatase of NSP3 from SARS CoV-2 (PDB id: 6W02)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 ADP ribose phosphatase of NSP3 from SARS CoV-2 in its APO form (PDB id: 6W02, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W02_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W02_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W02_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W02_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W02_SIRAHcg_prot.prmtop 6W02_SIRAHcg_prot.ncrst 6W02_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Exequiel Barrera (ebarrera@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: SARS-Cov2 Spike´s RBD / ACE2-B0AT1 complex (PDB id:6M17)
<p>This dataset contains the trajectory of a 3 microseconds-long coarse-grained molecular dynamics simulation of the hexameric complex between SARS-CoV2 Spike´s RBD, ACE2, and B0AT1 (PDB id: 6M17). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6M17_SIRAHcg_rawdata_0-1.tar, 6M17_SIRAHcg_rawdata_1-2.tar, and 6M17_SIRAHcg_rawdata_2-3.tar contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M17_SIRAHcg_3us_prot.tar contains only the protein coordinates, while 6M17_SIRAHcg_3us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M17_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M17_SIRAHcg_prot.prmtop 6M17_SIRAHcg_prot.ncrst 6M17_SIRAHcg_3us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Glycosylated RBD
<p>This dataset contains the trajectories of 10 microseconds-long coarse-grained molecular dynamics simulations of SARS-CoV2 Spike´s RBD glycosylated at Asn331 and Asn343. The initial coordinates correspond to amino acids 327 to 532 taken from the PDB structure 6VSB. Missing loops and glycosylation trees were added with CHARMM-GUI (http://www.charmm-gui.org).</p> <p>There are two different sets of simulations corresponding to Core Complex and High Mannose. Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycan parameters are available upon request.</p> <p>The files RBD-Man9_SIRAHcg_rawdata_0-6us.tar and RBD-Man9_SIRAHcg_rawdata_6-10us.tar, contain all the raw information required to visualize (on VMD), analyze, and backmap the simulations of High Mannose glycosylated RBD. Analogous information for Core-complex glycosylations is contained in files RBD-Core-complex_SIRAHcg_rawdata_0-6us.tar and RBD-Core-complex_SIRAHcg_rawdata_6-10us.tar</p> <p>Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, files with names ending in SIRAHcg_10us_glycoprot.tar contain only the protein coordinates, while file names ending with SIRAHcg_10us_glycoprot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at a trajectory:</p> <p>1- Untar the file RBD-Core-complex_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd glyco-RBD_SIRAHcg_glycoprot.prmtop glyco-RBD_SIRAHcg_glycoprot.ncrst glyco-RBD_SIRAHcg_glycoprot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
edexter/MAR: Initial release of project
<p>Data and code pertaining "Modeling the trophic impacts of invasive zooplankton in a highly invaded river"</p>
SIRAH-CoV2 initiative: UPDATED TRAJECTORY of SARS-Cov2 Spike´s RBD / ACE2-B0AT1 complex (PDB id:6M17)
<p>This dataset contains an updated trajectory of a four microseconds-long coarse-grained molecular dynamics simulation of the hexameric complex between SARS-CoV2 Spike´s RBD, ACE2, and B0AT1 (PDB id: 6M17). It substitutes the previous one on the same system, which was performed in the absence of disulfide bridges.</p> <p>Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6M17_SIRAHcg_rawdata_0-1.tar, 6M17_SIRAHcg_rawdata_1-2.tar, 6M17_SIRAHcg_rawdata_2-3.tar, and 6M17_SIRAHcg_rawdata_3-4.tar contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M17_SIRAHcg_4us_prot.tar contains only the protein coordinates, while 6M17_SIRAHcg_4us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M17_SIRAHcg_4us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M17_SIRAHcg_prot.prmtop 6M17_SIRAHcg_prot.ncrst 6M17_SIRAHcg_4usprot_skip.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: S2 Spike core fragment in postfusion state (PDB id:6M1V)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Spike S2 fragment in its postfusion form (PDB id: 6M1V). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M1V_SIRAHcg_rawdata_0-5us.tar, and 6M1V_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD 1.9.3), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M1V_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6LU7_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M1V_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M1V_SIRAHcg_prot.prmtop 6M1V_SIRAHcg_prot.ncrst 6M1V_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Source code for models of floral initiation in pea and gene expression data extracted from published sources
<p>The dataset contains the source code for computational models of a gene network controlling transition to flowering in pea (<em>Pisum sativum</em>). The models were based on ordinary differential equations (ODE) or neural networks. It also includes data on the expression dynamics of genes involved in the network, which was used for model fitting. The expression data was extracted from the following papers: </p> <p>Hecht, V., Laurie, R. E., Schoor, K. Vander, Ridge, S., Knowles, C. L., Liew, L. C., Sussmilch, F. C., et al. (2011). The Pea GIGAS Gene Is a FLOWERING LOCUS T Homolog Necessary for Graft-Transmissible Specification of Flowering but Not for Responsiveness to Photoperiod. 23, 147–161. doi:10.1105/tpc.110.081042</p> <p>Sussmilch, F. C., Berbel, A., Hecht, V., Schoor, K. Vander, Ferrándiz, C., Madueño, F., et al. (2015). Pea VEGETATIVE2 Is an FD Homolog That Is Essential for Flowering and Compound In fl orescence Development. 27, 1046–1060. doi:10.1105/tpc.115.136150</p> <p>The source code of the DEEP software used for parameter optimization in the model fitting can be found in the Gitlab repository (https://gitlab.com/mackoel/deepmethod/-/tree/master).</p> <p>The files are the supplement to the following manuscript, submitted to Frontiers in Genetics:</p> <p>"Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in <em>Pisum sativum</em>" by Polina Pavlinova, Maria G. Samsonova, and Vitaly V. Gursky.</p> <p>All possible questions can be sent to: Polina Pavlinova (polina.pavlina1004@gmail.com), Vitaly Gursky (gursky@math.ioffe.ru).</p>
Local adaptation from afar: migratory bird populations diverge in the initiation of reproductive timing while wintering in sympatry
<p><span>The initiation of reproduction in many seasonally breeding animals is controlled by photoperiod and tends to be clinal: populations at higher latitudes breed later than those at lower latitudes, often reflecting a higher photoperiodic threshold. Migratory animals presumably time reproduction to match conditions at their breeding grounds at least in part by cues perceived</span> on their wintering grounds<span>. </span>We asked how closely related dark-eyed junco (<i>Junco hyemalis</i>) populations that overwinter in sympatry but breed in allopatry respond to their shared winter environment by comparing early spring indices of readiness to migrate and breed (baseline and elevated testosterone). We measured stable hydrogen isotopes from feathers grown the preceding year and claws grown during winter to estimate breeding and wintering latitudes, respectively. We predicted that if reproductive initiation is adapted to the emergence of resources at their respective breeding destinations, then birds migrating to higher latitudes (slate-colored junco; <i>J. h. hyemalis</i>) should delay breeding as compared to those migrating to lower latitudes (pink-sided junco; <i>J. h. mearnsi</i>) despite a common overwinter environment. We found higher testosterone in pink-sided juncos consistent with earlier reproductive initiation, suggesting local adaptation in reproductive phenology achieved through differential responses to predictive environmental cues.</p>
SIRAH-CoV2 initiative: SARS-CoV-2 helicase (PDB id:6ZSL)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Helicase protein (PDB id:6ZSL). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Parameters for Zinc ions are those reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6ZSL_SIRAHcg_rawdata_0-4us.tar, 6ZSL_SIRAHcg_rawdata_4-8us.tar, and 6ZSL_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (using VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6ZSL_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6ZSL_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6ZSL_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6ZSL_SIRAHcg_prot.prmtop 6ZSL_SIRAHcg_prot.ncrst 6ZSL_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Raw data for the article "Azide Radical Initiated Ring Opening of Cyclopropenes Leading to Alkenyl Nitriles and Polycyclic Aromatic Compounds"
<p>Raw NMR, MS and IR data for the article "Azide Radical Initiated Ring Opening of Cyclopropenes Leading to Alkenyl Nitriles and Polycyclic Aromatic Compounds" published in Angewandte Chemie: </p> <p>https://onlinelibrary.wiley.com/doi/10.1002/anie.202013516 </p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p> <p> </p>
SIRAH-CoV2 initiative: RBD triple glycosylated at Asn331, 343, and 481 from PDB structure 6XEY
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of a Spike's RBD from SARS-CoV2 glycosylated at Asn331, 343, and 481 with Man9 glycosylation trees. The initial coordinates correspond to amino acids 327 to 532 taken from the PDB structure 6XEY. Missing loops and glycosylation trees were added with CHARMM-GUI (http://www.charmm-gui.org). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycan parameters correspond to those reported by <a href="https://www.biorxiv.org/content/10.1101/2020.12.18.423446v1">Garay et al</a>.</p> <p>The files 6XEY-RBD-3Man9_SIRAHcg_0-4us.tar, 6XEY-RBD-3Man9_SIRAHcg_4-8us.tar, and 6XEY-RBD-3Man9_SIRAHcg_8-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_10us.tar contains only the protein coordinates, while 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6XEY-RBD-3Man9_SIRAHcg_glycoprot.prmtop 6XEY-RBD-3Man9_SIRAHcg_glycoprot.ncrst 6XEY-RBD-3Man9_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Phenanthrene: TD-DFTB datasets, pre-trained SchNet models and initial coniditions for TSH
<p><em>Data associated with the paper entitled </em></p> <p><strong>On application of Deep Learning to simplified quantum-classical dynamics in electronically excited states</strong></p> <ol> <li>Three TD-DFTB datasets (<strong>sX_10_force.db</strong>) have been produced using the <a href="https://wiki.fysik.dtu.dk/ase/">Atomic Simulation Environment</a> (ASE) coupled to <a href="http://demon-nano.ups-tlse.fr/">deMon-Nano</a> code for the linear response Time-Dependent Density Functional based Tight-Binding (TD-DFTB) calculations. Each dataset contains 10000 TD-DFTB electronic structure calculations for a given excited singlet state (S<sub>2</sub>/S<sub>3</sub>/S<sub>4</sub>) of a neutral phenanthrene molecule. Each database entry contains Cartesian atomic coordinates as well as potential energy and atomic forces for a given excited state at a given geometry. Since ASE has been used, all physical quantities are stored in the corresponding units (e.g. eV for energy or eV/Å for forces). The file format is SQLite as provided by the ASE;</li> <li>Three pre-trained Deep Learning models (<strong>best_model_sX</strong>) for a given excited singlet state have been produced using <a href="https://schnetpack.readthedocs.io/en/stable/">SchNetPack</a> package, which implements the SchNet architecture for atomistic simulations. Each model has been trained using the corresponding TD-DFTB dataset from #1. The file format is binary as provided by the SchNetPack;</li> <li><a href="https://zenodo.org/api/files/f1925cb5-66a8-4c6f-809b-3414f0cbc1d5/500_init_conditions.tar.gz"><strong>500_init_conditions.tar.gz</strong> </a> contains 500 initial conditions (Cartesian coordinates and velocities), which can be used for Trajectory Surface Hopping (TSH) simulations with or without the pre-trained models from #2.</li> </ol> <p> </p>
SMS-GraphQL Initial Primary Studies Dataset
<p>SMS-GraphQL Initial Primary Studies Dataset</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.