cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3754464
title: Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints

description: <strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong> <strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); https://doi.org/10.1063/5.0019088 (2020) arXiv:2006.11066 <strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/) <strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory <strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036) <strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd) The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) ( *csv files were added erroneously and do not belong to the project)
DOI: 10.25612/837.l6eexko5ym3g
title: Harris County Evictions

description: Harris County eviction data curated by January Advisors and the Urban Data Platform. Case-level data from 2010 to 2020, geocoded at the census block level.
DOI: 10.21227/ygff-np71
title: Aerial images for pile fire detection using drones (UAVs)

description: Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile fire in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.
DOI: 10.25612/837.8vy90znvvyl8
title: Harris County Evictions
title: Evictions in Harris County

description: Eviction data obtained from Harris County Justice Courts and curated by January Advisors
DOI: 10.21227/h44p-nn36
title: Aerial images for pile fire detection using drones (UAVs)

description: Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile fire in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.
DOI: 10.25612/837.lky5x5v4qma1
title: Harris County Evictions per Community Tabulation Area (2010 and 2019)
title: Evictions per Community Tabulation Area (2010 and 2019) in Harris County

description: Counts of cases with judgement and cases which resulted in an eviction judgement per Community Tabulation Area (CTA) in Harris County for 2010 and 2019
DOI: 10.35013/g34a-xn60
title: Voting Behavior in the Wisconsin State Legislature: 2019-2020
DOI: 10.35013/8451-br24
title: Voting Behavior in the Wisconsin State Legislature: 2011-2018, Machine Rule
DOI: 10.35013/rptx-5f04
title: Voting Behavior in the Wisconsin State Legislature: 2003-2010, Gridlock
DOI: 10.6092/unibo/amsacta/6481
title: MADFORWATER. WP2 Adaptation of wastewater treatment technologies for agricultural reuse. Task2.4 Industrial wastewater treatment. Textile wastewater

description: This data set includes data related to the treatment of textile wastewater (TWW), obtained in the framework of MADFORWATER project. In particular, the data set includes experimental data produced in laboratory relative to the optimization of textile dye decolorization by bacterial consortium. Also, it includes data from experimental observations related to another technology for textile dyes removal: a two-stage (solids separation stage anaerobic/aerobic) moving bed biological bioreactor (MBBR). Three types of potential users are envisaged for this dataset: (i) researchers active in the field of TWW treatment and valorization, that will use these data to design their experiments; (ii) textile industries; (iii) companies active in the fields of wastewater (WW) treatment and valorization.
DOI: 10.6092/unibo/amsacta/6503
title: MADFORWATER. WP2 Adaptation of wastewater treatment technologies for agricultural reuse. Task2.2 Municipal wastewater and drainage canal water treatment

description: This data set includes data produced in the framework of MADFORWATER relatively to the treatment of municipal wastewater (WW) and drainage canal water. Specifically, it includes experimental observations gathered from the operation of Constructed wetlands (CWs) for the removal of heavy metals, emerging pollutants and nutrients. Moreover, it includes also experimental data relative to the secondary treatment of municipal wastewater (WW) using trickling filters filled with innovative high specific-surface carriers. Potential users for this dataset may be: (i) researchers active in the field of municipal WW treatment and valorization, and (ii) companies active in the fields of WW treatment and valorization.
DOI: 10.35013/xr6q-8m72
title: Voting Behavior in the Wisconsin State Legislature: Wisconsin's Most Recent Golden Age, 1965-1966
DOI: 10.35013/k14c-pc04
title: Puerto Rican Elderly: Health Conditions (PREHCO) Wave 2, 2006-2007
DOI: 10.35013/qhs2-4y94
title: Puerto Rican Elderly: Health Conditions (PREHCO) Wave 1, 2002-2003
DOI: 10.35013/jp59-p875
title: Online Politics and Young Voters 2006: The Use of Political Websites by American Youth and Effects on Political Engagement
DOI: 10.35013/ez5e-sa46
title: Lumbertowns in the Cutover: Economic Cross-Sections of 94 (Mostly Obscure) Wisconsin and Michigan Communities at 4 Decade Long Intervals
DOI: 10.35013/zxq4-7j18
title: National Health Measurement Study (NHMS), 2005-2006
DOI: 10.35013/mbqr-k303
title: Slave Movement During the Eighteenth and Nineteenth Centuries
DOI: 10.35013/hj0a-wp61
title: Capital gains on common stock for respondents of the surveys of financial characteristics, 1963, and of changes in family finances, 1964
DOI: 10.35013/a6pc-7y84
title: American Catholic priesthood survey, 1971
DOI: 10.35013/nf2c-vq16
title: Growth of American families, 1960
DOI: 10.5519/qd.momgff6o
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4676697 records)
DOI: 10.35013/rr8j-7t68
title: Growth of American families: Single Women, 1955
DOI: 10.35013/7z47-m624
title: Migrant farm workers in Wisconsin, 1989
DOI: 10.6084/m9.figshare.13262690
title: Updated NMA of drugs to prevent fragility fractures

description: Updated results
DOI: 10.6084/m9.figshare.13262690.v1
title: Updated NMA of drugs to prevent fragility fractures

description: Updated results
DOI: 10.5281/zenodo.4281369
title: Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study

description: <strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong> include simulation of the binding pocket druggability, physical-chemical properties, and the binding site composition Protease_clean.ipynb - Jupyter Notebook containing analysis of the generated data allTables.zip - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods Every10-ligand_6LU7_R3.5.zip - results of TRAPP pocket analysis on the MD frames PDB-Giulia.zip - TRAPP pocket analysis of 40 PDB complexes of main protease TRAPP_properties_PDB.xlsx - binding pocket properties for 40 PDB complexes of main protease summarized in a table DrugPDB_3structures.xlsx - binding pocket properties for 3 PDB structures <strong>2. Docking Results</strong> TRAPP_secondSelection_VS.csv - docking of selected structures from TRAPP analysis Fred_VS.csv - docking of PDB structures using Fred Glide_VS.csv - docking of PDB structures using Glide
DOI: 10.15472/nmdwom
title: Anfibios asociados a áreas degradadas por minería en Jigualito, Chocó-Colombia

description: La minera a cielo abierto es una actividad que crea alteraciones en el medio natural, desde las más imperceptibles hasta las que representan claros impactos sobre la cobertura terrestre, con fuerte impacto visual por cambios en el paisaje circundante relacionado con alteraciones en los cuerpos de agua, deforestación y degradación de suelos, entre otros (ONUDC y Gobierno de Colombia 2016). Esta actividad, que hace algunos años no se percibía como un factor de riesgo, hoy se contempla con gran preocupación, ya que los impactos a nivel ambiental y social que esta actividad ocasiona, han hecho que crezca la conciencia de la necesidad de regularlos. Por lo tanto, es necesario adelantar investigaciones científicas que permitan entender como dichos impactos se manifiestan en el ambiente y su consecuente efecto a corto y largo plazo en los ecosistemas, en la fauna y sobre todo en grupos de baja movilidad y alta sensibilidad como los anfibios. La minería es una actividad que genera fragmentación en los ecosistemas del departamento del Chocó y algunos impactos causados por esta se pueden apreciar en: 1) tala de los bosques y deterioro de suelos, 2) destrucción de fuentes hídricas, 3) cambios en cauces de ríos y quebradas, 4) contaminación con aceites y mercurio, 5) cambios considerables al paisaje natural, y 6) pérdida de biodiversidad y diversidad genética por intervención y destrucción de ecosistemas frágiles (Silvera, 2011). Estas alteraciones afectan directamente grupos faunísticos terrestres, como aves y mamíferos que requieren de hábitats continuos para mantener sus poblaciones viables, observándose estos efectos muchos más fuertemente, en grupos de poca movilidad y gran sensibilidad como los anfibios y reptiles que dependen exclusivamente del componente vegetal el cual les brinda disponibilidad de hábitats, además de jugar un papel importante en sus procesos tróficos y reproductivos (Quesada 2015). El presente estudio pretende evaluar los efectos o impactos causados por la minería a cielo abierto sobre la diversidad de anfibios, en un bosque pluvial tropical en el departamento del Chocó, considerando la utilización de cuatro escenarios con distintos tiempos desde la intervención. Además se espera con este estudio obtener información respecto al tiempo de re-aparición de las especies, con lo cual se quiere aportar datos que puedan ser útiles para conocimiento, manejo y recuperación de este importante grupo taxonómico y de los ecosistemas perturbados por minería en esta región.
DOI: 10.18112/openneuro.ds003387.v1.0.0
title: Monash-vis2PET-MR
DOI: 10.18112/openneuro.ds003386.v1.0.0
title: Monash-vis2PET-MR
DOI: 10.6084/m9.figshare.13260491
title: P-wave velocity and density models of Montserrat

description: P-wave velocity model and density model obtained by joint inversion of seismic traveltimes and gravity anomalies as described in Paulatto et al. (2019).The traveltime data come from the SEA-CALIPSO experiment and are described in Paulatto et al. (2012). The gravity data are a compilation of land and marine gravity data. The code used for the inversion (jif3D, https://sourceforge.net/projects/jif3d/) is described in Moorkamp et al. (2010). The authors welcome reuse of the models and data. For any questions contact Michele Paulatto (m.paulatto@imperial.ac.uk).<br><b>Included files:</b>monji_ttcx_1d0dd.nc = traveltime datasetmonji_ga5_grad_f500.nc = gravity anomaly datasetresult.tomo.inv.nc = output slowness fileresult.grav.inv.nc = ourput density anomaly fileresult.dens.inv.nc = output density file (density anomaly + reference 1d density model)paperzsections_2_4_6.pdf = plots of horizontal cross-sectionspvsection_23_23.5.pdf = plots of vertical cross-sections<br><br><b>Coordinates and units</b>The model coordinates are defined over a local Cartesian coordinate system with origin at longitude -62.4 degrees, latitude 16.5 degrees. Easting, northing and depth are in units of metres from the origin point. Depth is referenced to an elevation of 2000 m above mean sea level and is positive downward (zero Depth corresponds to 2000 m elevation). The Slowness field is in units of s/m and the density is in units of g/cm<sup>3</sup>.<br>Northing bounds: 0 / 50000Easting bounds: 0 / 45000Depth bounds: 0 / 14500 (-2000 / 12500 )<br><b>Reading the models</b>The contents can be loaded in Python using the netcdf4 library. See example code snippet below.<br> from netCDF4 import Dataseta = Dataset('result.tomo.inv.nc', mode='r')northing = a.variables['Northing'][:]easting = a.variables['Easting'][:]depth = a.variables['Depth'][:]slowness = a.variables[Slowness][:,:,:]a.close()<br>b = Dataset('result.dens.inv.nc', mode='r')northing = b.variables['Northing'][:]easting = b.variables['Easting'][:]depth = b.variables['Depth'][:]density = b.variables[Density][:,:,:]b.close()<br>c = Dataset('result.grav.inv.nc', mode='r')northing = c.variables['Northing'][:]easting = c.variables['Easting'][:]depth = c.variables['Depth'][:]density_a = c.variables[Density][:,:,:]c.close()<br>
DOI: 10.6084/m9.figshare.13260491.v1
title: P-wave velocity and density models of Montserrat

description: P-wave velocity model and density model obtained by joint inversion of seismic traveltimes and gravity anomalies as described in Paulatto et al. (2019).The traveltime data come from the SEA-CALIPSO experiment and are described in Paulatto et al. (2012). The gravity data are a compilation of land and marine gravity data. The code used for the inversion (jif3D, https://sourceforge.net/projects/jif3d/) is described in Moorkamp et al. (2010). The authors welcome reuse of the models and data. For any questions contact Michele Paulatto (m.paulatto@imperial.ac.uk).<br><b>Included files:</b>monji_ttcx_1d0dd.nc = traveltime datasetmonji_ga5_grad_f500.nc = gravity anomaly datasetresult.tomo.inv.nc = output slowness fileresult.grav.inv.nc = ourput density anomaly fileresult.dens.inv.nc = output density file (density anomaly + reference 1d density model)paperzsections_2_4_6.pdf = plots of horizontal cross-sectionspvsection_23_23.5.pdf = plots of vertical cross-sections<br><br><b>Coordinates and units</b>The model coordinates are defined over a local Cartesian coordinate system with origin at longitude -62.4 degrees, latitude 16.5 degrees. Easting, northing and depth are in units of metres from the origin point. Depth is referenced to an elevation of 2000 m above mean sea level and is positive downward (zero Depth corresponds to 2000 m elevation). The Slowness field is in units of s/m and the density is in units of g/cm<sup>3</sup>.<br>Northing bounds: 0 / 50000Easting bounds: 0 / 45000Depth bounds: 0 / 14500 (-2000 / 12500 )<br><b>Reading the models</b>The contents can be loaded in Python using the netcdf4 library. See example code snippet below.<br> from netCDF4 import Dataseta = Dataset('result.tomo.inv.nc', mode='r')northing = a.variables['Northing'][:]easting = a.variables['Easting'][:]depth = a.variables['Depth'][:]slowness = a.variables[Slowness][:,:,:]a.close()<br>b = Dataset('result.dens.inv.nc', mode='r')northing = b.variables['Northing'][:]easting = b.variables['Easting'][:]depth = b.variables['Depth'][:]density = b.variables[Density][:,:,:]b.close()<br>c = Dataset('result.grav.inv.nc', mode='r')northing = c.variables['Northing'][:]easting = c.variables['Easting'][:]depth = c.variables['Depth'][:]density_a = c.variables[Density][:,:,:]c.close()<br>
DOI: 10.5066/f74b3081
title: Aquatic Macroinvertebrate Data Collected from Kansas City Metro Area Streams, 2011-2013

description: During Fall 2011 (Oct. 3-6, 2011), Spring 2012 (March 26-28, 2012), Spring 2013 (April 28-30, 2013), and Fall 2013 (Oct. 22-25, 2013), the USGS-CERC (Barry Poulton) and the Kansas City Water Services Dept. (Jing Tao) sampled stream-dwelling aquatic macroinvertebrate communities in nine watersheds within the non-connected stormwater management region in Kansas City, Missouri, utilizing the Missouri bioassessment protocols. Samples were taken with a rectangular-framed net (500&micro;m mesh netting) from 3 stream habitats (coarse substrate in cobble riffles, depositional non-flow habitat in pools, and rootmats along shoreline areas). Field-preserved samples from each habitat were processed (subsampling with a gridded tray, sorting with 10X magnification under a microscope, and large/rare organisms removed for inclusion in estimates of taxonomic richness) in the laboratory, and macroinvertebrate organisms were enumerated and identified to the lowest practical taxonomic unit. Counts of organisms and recording of large/rare specimens present in the samples but not counted, were entered into electronic datasheets. The data represent counts and presence of aquatic macroinvertebrate specimens as a result of laboratory processing (sorting, enumeration and specimen identification) of the samples that were taken and preserved in the field.
DOI: 10.15472/yo3iyr
title: Artropofauna asociada a áreas degradadas por minería en jigualito, Condoto-Chocó.

description: En las áreas degradadas por la minería, el suelo constituye el principal aspecto a estudiar en los procesos de restauración ecológica. Por lo tanto, uno de los primeros pasos para procurar la restauración natural de áreas afectadas por minería es la recuperación de la estructura y funciones del suelo, lo cual en gran parte, es llevado a cabo por organismos presentes en este y cuyos procesos funcionales ayudan a su reconstrucción y mantenimiento (Cabrera 2014). En este medio, los organismos establecen interacciones que favorecen la funcionalidad e integridad del suelo; los artrópodos por ejemplo, desempeñan importantes funciones ecológicas, tales como el reciclaje de nutrientes, la descomposición de la materia orgánica y la conservación de la estructura del terreno, lo que garantiza la calidad y fertilidad del medio edáfico en sistemas naturales, agrícolas y forestales (Cabrera et al. 2011 y Brown et al. 2001), proporcionando así, valiosos servicios ecosistémicos. Adicionalmente, Muchos artrópodos son sensibles a las perturbaciones naturales y antrópicas del medio, (Ruiz 2007, Lavelle et al. 2003 y Pashanasi 2001), por tales motivos, son considerados como indicadores biológicos que permiten evaluar el estado de conservación del suelo. La disminución de la abundancia y diversidad de artrópodos conduce invariablemente a una pérdida de determinadas funciones importantes en el suelo, como es el caso de la degradación, reciclaje y reutilización de la hojarasca en el medio edáfico (Lavelle et al. 1993). Dichas características se pierden en un ecosistema por efecto de diversas alteraciones, sean naturales o antrópicas, estas últimas producen grandes cambios en el ambiente edáfico dependiendo del tipo y la intensidad de la actividad antrópica realizada. En este contexto, una de las actividades antrópicas que en la actualidad impacta más los suelos del Chocó, es la minería mecanizada, mediante la cual se retiran amplias hectáreas de cobertura vegetal y se remueven grandes volúmenes de tierra, propiciando la pérdida de su estructura (por efecto de la erosión, sedimentación, disgregación o compactación) y fertilidad (materia orgánica, nutrientes). Según Lavelle (2000) este tipo de perturbación propicia la disminución o pérdida de poblaciones de invertebrados clave, siendo gran parte de ellos artrópodos edáficos, acarreando además con la disminución de la funcionalidad y la integridad ecológica del suelo y con la capacidad productiva del mismo. Por lo anterior, se hace necesario emprender investigaciones relacionadas con el tema, lo que es un importante punto de partida para entender los efectos potenciales de dicha actividad en los suelos que componen los ecosistemas del Chocó. Por ello, esta investigación, tuvo por fin generar información útil para la planificación, gestión y reconstrucción del patrimonio natural del departamento del Chocó, con ello los bienes y servicios ambientales y económicos que le generan bienestar a la sociedad.
DOI: 10.5517/ccdc.csd.cc24rvxp
title: CCDC 1989143: Experimental Crystal Structure Determination

description: Related Article: Rodrigo Beltrán-Suito, Viktoria Forstner, J. Niklas Hausmann, Stefan Mebs, Johannes Schmidt, Ivelina Zaharieva, Konstantin Laun, Ingo Zebger, Holger Dau, Prashanth W. Menezes, Matthias Driess|2020|Chemical Science|11|11834|doi:10.1039/D0SC04384B
DOI: 10.5517/ccdc.csd.cc251zbk
title: CCDC 1997898: Experimental Crystal Structure Determination

description: Related Article: Ruihuan Liu, Dongxin Ma, Lian Duan|2020|J.Mater.Chem.C|8|14766|doi:10.1039/D0TC04126B