cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/ampgmw/oumitb
title: Hernan Cortes and the Conquest of the Aztec Empire.tab

description: :unav
DOI: 10.7910/dvn/ampgmw/hjnwep
title: Hernan Cortes and the Conquest of the Aztec Empire.tab

description: :unav
DOI: 10.5281/zenodo.3897353
title: MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins

description: MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.
DOI: 10.5281/zenodo.3897352
title: MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins

description: MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.
DOI: 10.21227/4s1g-e834
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Also: Coronavirus (COVID-19) Tweets Dataset (180+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3842295
title: Filling gaps of occurrence data for Colombia: case of birds

description: Biological databases from collections, museums, and inventories present gaps and biases as some species and geographical areas remain poorly known. Biological expeditions are thus crucial to increase the knowledge of biodiversity and should be planned carefully. Using 1-km2 spatial resolution, this dataset provides which locations of Colombia that have been surveyed at least one time for the bird group. <br> The first two columns describe the latitudinal (lat) and longitudinal (long) coordinate of the locations. Meanwhile, the last column (observed) describes whether the location has been surveyed at least one time, e.g., if the location has at least a point record, its value is 1; otherwise, its value is 0.
DOI: 10.5281/zenodo.3842294
title: Filling gaps of occurrence data for Colombia: case of birds

description: Biological databases from collections, museums, and inventories present gaps and biases as some species and geographical areas remain poorly known. Biological expeditions are thus crucial to increase the knowledge of biodiversity and should be planned carefully. Using 1-km2 spatial resolution, this dataset provides which locations of Colombia that have been surveyed at least one time for the bird group. <br> The first two columns describe the latitudinal (lat) and longitudinal (long) coordinate of the locations. Meanwhile, the last column (observed) describes whether the location has been surveyed at least one time, e.g., if the location has at least a point record, its value is 1; otherwise, its value is 0.
DOI: 10.5281/zenodo.3892995
title: Dataset for "A cross–nearest neighbor/Monte Carlo algorithm for single molecule localization microscopy defines interactions between p53, Mdm2, and MEG3"

description: Dataset for "A cross–nearest neighbor/Monte Carlo algorithm for single molecule localization microscopy defines interactions between p53, Mdm2, and MEG3" p53-Mdm2 Well A: +Dox -Nut Well B: -Dox -Nut Well C: +Dox +Nut Well D: -Dox +Nut p53-MEG3 Well A: MEG3 +Dox Well B: MEG3 -Dox Well C: GAPDH +Dox Well D: GAPDH -Dox
DOI: 10.21227/xcsj-s552
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for continuous level-ground walking. Building on previous research, this work analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee joint (0.8 ± 0.3 W/kg) and ankle joint (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.36 ± 0.06 J/kg, 0.16 ± 0.08 J/kg, and 0.03 ± 0.01 J/kg, respectively. Assuming 80-kg bodyweight and previous regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~28 Joules of total electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Future research should focus on activity tracking and biomechanical analyses of seniors and rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.21227/c4r9-cb12
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Also: Coronavirus (COVID-19) Tweets Dataset (180+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3897454
title: Codes and Dataset for "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"

description: This file contains the codes and dataset pertaining to the manuscript "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"
DOI: 10.5281/zenodo.3691832
title: Codes and Dataset for "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"

description: This file contains the codes and dataset pertaining to the manuscript "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"
DOI: 10.7910/dvn/votj9b/shecfe
title: 19ja018_archival_manuscript.pdf
DOI: 10.7910/dvn/fjyc0k/tgtir5
title: 18ja063_archival_manuscript.pdf
DOI: 10.5281/zenodo.3897301
title: Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints

description: 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) <pre><code>2YKI - protein-ligand complex , PDB ID 2YKI - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/) - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/) 6EI5 - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5 - ref-min.pdb minimized structure - ref.prmtop topology file - moe.mol2 - ligand structure in mol2 format - amber2namd2.dcd generated MD trajectory The rest of the files contains data obtained from simulation of the complex of GPCR muscarinic receptor M2 (PDB ID:4MQT; immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. - IXO.pdb and moe.mol2 - PDBand MOL2 structure of iperoxo - AMBER_eq.tar.gz - structure of the equilibrated complex generated using AMBER software - NAMD_eq.tar.gz - two equilibration trajectories in dcd format generated using NAMD software - RAMD_eq.tar.gz - 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) SAD_3-RAMD-03-2020.pkl - 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</code></pre>
DOI: 10.26300/3rq2-ts68
title: Supplementary Materials and Code for "Positive Beliefs and the Likelihood of Successful Community Discharge from Skilled Nursing Facilities"
DOI: 10.5519/qd.vpj7znhz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (647 records)
DOI: 10.15468/zzycrx
title: Lestes sublatus

description: This dataset contains the digitized treatments in Plazi based on the original journal article Muzon, J. (1993): Lestes sublatus. Notul. Odonotol. 4: 19-20, DOI: http://doi.org/10.5281/zenodo.3585609
DOI: 10.7910/dvn/brznjg/cg6kdi
title: DavidBarrett-FazekasCorruptionDisplacement_replicaton.tab

description: Stata dataset
DOI: 10.15161/oar.it/30435
title: Infectious disease modelling and the dynamics of the active cases - Data

description: We developed a model that tries to describe the dynamics of the spread of a disease among a population, in particular the progress of infected active cases. The model is then applied to describe Italy CoViD-19 outbreak and subsequently, we tried to predict possible scenarios.
DOI: 10.5281/zenodo.3895334
title: Input parameters, output data, and script to calculate strain and velocity from fault source parameters

description: This archive contains two data tables, one script, and one dataset to accompany the paper: <strong>Strain and velocity across the Great Basin derived from 15-ka fault slip rates: Implications for continuous deformation and seismic hazard in the Walker Lane</strong> <em>Nadine G. Reitman<sup>1 </sup> and Peter Molnar<sup>1,2</sup></em> <sup>1</sup>Department of Geological Sciences, University of Colorado Boulder <sup>2</sup>Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado Boulder The data tables contain the input source fault parameters (fault strike, dip, rake, length, location, and slip rate) and the contribution to strain calculated for each fault relative to 317 degrees using the associated script. The script uses the methods of Kostrov (1974) and Haines (1982) to calculate strain and velocity across the Great Basin using slip rates reported in the USGS National Seismic Hazard Map (2020 update of the 2014 data, Petersen et al, 2014) and Pérouse & Wernicke (2017). The script is written in Python3 and requires the user to have some knowledge of how to run Python code. The included dataset (hazfaults_clean.p) contains the data to run the script for the USGS fault sources. Please contact nadine.reitman@colorado.edu with questions.
DOI: 10.5281/zenodo.3895333
title: Input parameters, output data, and script to calculate strain and velocity from fault source parameters

description: This archive contains two data tables, one script, and one dataset to accompany the paper: <strong>Strain and velocity across the Great Basin derived from 15-ka fault slip rates: Implications for continuous deformation and seismic hazard in the Walker Lane</strong> <em>Nadine G. Reitman<sup>1 </sup> and Peter Molnar<sup>1,2</sup></em> <sup>1</sup>Department of Geological Sciences, University of Colorado Boulder <sup>2</sup>Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado Boulder The data tables contain the input source fault parameters (fault strike, dip, rake, length, location, and slip rate) and the contribution to strain calculated for each fault relative to 317 degrees using the associated script. The script uses the methods of Kostrov (1974) and Haines (1982) to calculate strain and velocity across the Great Basin using slip rates reported in the USGS National Seismic Hazard Map (2020 update of the 2014 data, Petersen et al, 2014) and Pérouse & Wernicke (2017). The script is written in Python3 and requires the user to have some knowledge of how to run Python code. The included dataset (hazfaults_clean.p) contains the data to run the script for the USGS fault sources. Please contact nadine.reitman@colorado.edu with questions.
DOI: 10.5281/zenodo.3895807
title: W2S: Widefield2SIM Raw Dataset

description: This dataset contains the raw images of W2S (https://arxiv.org/abs/2003.05961). W2S has 360 (120 FOV x 3 channels) sets of human cell images. Each image set contains 400 Widefield shots, 15 (3 phases x 5 shots) shots of SIM inputs and 1 SIM reconstructed result. Repository: https://github.com/widefield2sim/w2s
description: {"references": ["https://arxiv.org/abs/2003.05961"]}
DOI: 10.5281/zenodo.3895806
title: W2S: Widefield2SIM Raw Dataset

description: This dataset contains the raw images of W2S (https://arxiv.org/abs/2003.05961). W2S has 360 (120 FOV x 3 channels) sets of human cell images. Each image set contains 400 Widefield shots, 15 (3 phases x 5 shots) shots of SIM inputs and 1 SIM reconstructed result. Repository: https://github.com/widefield2sim/w2s
description: {"references": ["https://arxiv.org/abs/2003.05961"]}
DOI: 10.7910/dvn/u2hvb6/9rlc54
title: A__1__20170510_092556.wav

description: :unav
DOI: 10.17614/q4bk17t3x
title: myrtillin
DOI: 10.3886/e113962v1
title: Replication data for: The Ongoing Evolution of US Retail: A Format Tug-of-War

description: The past 15-20 years have seen substantial and visible changes in the way US retail business is conducted. Explanations about what is happening in the retail sector have been dominated by two powerful and not fully consistent narratives: a prediction that retail sales will migrate online and physical retail will be virtually extinguished, and a prediction that future shoppers will almost all be heading to giant physical stores like warehouse clubs and supercenters. Although online retail will surely continue to be a force shaping the sector going forward and may yet emerge as the dominant mode of commerce in the retail sector in the United States, its time for supremacy has not yet arrived. We discuss evidence indicating that the warehouse clubs/supercenter format has had a greater effect on the shape of retail over the past 15-20 years. We begin with an overview of the retail sector as a whole, which over the long term has been shrinking as a share of total US economic activity and in terms of relative employment share. The retail sector has experienced stronger-than average productivity growth, but this has not been accompanied by commensurate wage growth. After discussing the important e-commerce and warehouse clubs/supercenters segments, we look more broadly at changes across the structure of the retail sector, including scale, concentration, dynamism, and degree of urbanization. Finally, we consider the likely future course of the retail sector.
DOI: 10.5281/zenodo.3538298
title: Differential Rotation in Convective Envelopes: Constraints from Eclipsing Binaries

description: Data, inlists, and scripts associated with doi:10.1093/mnras/stz2983
DOI: 10.13145/bacdive106048.20180622.3
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.14457/aru.the.2008.63
title: ปัจจัยที่มีผลต่อการตัดสินใจเลือกซื้อรถบรรทุกใหม่ [กระบะ 1 ตัน] ของผู้บริโภค ในจังหวัดพระนครศรีอยุธยา /
DOI: 10.23725/c4ge-5639
title: NWD635384.freeze5.v1.vcf.gz.csi

description: TOPMed: NWD635384.freeze5.v1.vcf.gz.csi <br>File: VCF CSI file
DOI: 10.25491/g0mm-vz05
title: GTEX-ZAB4-1026-SM-5HL7T

description: Sample ID: GTEX-ZAB4-1026-SM-5HL7T
DOI: 10.21942/uva.12301106
title: AHEAD template

description: Group template created from the subjects of the AHEAD database, for quantitative R1, quantitative R2* and QSM.<br>
DOI: 10.13012/b2idb-0598724_v1
title: Data from: Classical Topological Order in the Kinetics of Artificial Spin Ice

description: The dataset includes the data used in the study of Classical Topological Order in the Kinetics of Artificial Spin Ice. This includes the photoemission electron microscopy intensity measurement of artificial spin ice at different temperatures as a function of time. The data includes the raw data, the metadata, and the data cookbook. Please refer to the data cookbook for more information. Note: vertex_population.xlsx file in the meta_data_code folder can be disregarded.
DOI: 10.21421/d2/3mmmlw
title: Phenotypic evaluation data of medium duration multi location Pigeonpea hybrids trial
DOI: 10.26131/irsa161
title: COSMOS Photometric Redshift Catalog

description: COSMOS is an astronomical survey designed to probe the formation and evolution of galaxies as a function of cosmic time (redshift) and large scale structural environment. The survey covers a 2 square degree equatorial field with imaging by most of the major space-based telescopes (Hubble, Spitzer, GALEX, XMM, Chandra) and a number of large ground based telescopes (Subaru, VLA, ESO-VLT, UKIRT, NOAO, CFHT, and others). Over 2 million galaxies are detected, spanning 75% of the age of the universe. This dataset or service is made available by the Infrared Science Archive (IRSA) at IPAC, which is operated by the California Institute of Technology under contract with the National Aeronautics and Space Administration.
DOI: 10.17909/t9-x2t7-5j69
title: Data from GO-14873 visit 2

description: This data is being published in Wakeford et al. submitted to AJ November 2018
DOI: 10.15784/601065
title: List of samples of WAIS Divide and Byrd (deep) ice that were analyzed for radiogenic isotopes at LDEO
DOI: 10.26304/366-u1497a-mspoint
title: Expedition 366 Hole U1497A Magnetic susceptibility (section half) data file

description: Hole U1497A MSPOINT - Magnetic susceptibility (section half) for Expedition 366 Hole U1497A
DOI: 10.25573/data.10042631.v3
title: Fortuna, Clearing_Wind Speed, Manual

description: Fortuna station (Centro de Investigaciones Jorge L. Arauz)<br>South Clearing<br>Location: 8° 43.340'N, 82° 14.241'W<br>Wind Speed, Daily wind speed calculated from Totalizing Anemometer wind passage<br><br>Located in the highlands of the Chiriqui Province, in western Panama.There are three sensor locations: north clearing, south clearing, and a 15m tower.<br>
DOI: 10.18112/openneuro.ds000236.v2.0.1
title: Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
DOI: 10.25550/tmg
title: microMRI images of skulls of K14-Cre;Tgfbr2fl/fl mice at E18.5

description: microMRI images of skulls of K14-Cre;Tgfbr2fl/fl mice at E18.5. The dataset contains 3-D images in niftii format for 1 mouse.
DOI: 10.20375/0000-000b-c9aa-3
title: Universal-Kirchenzeitung-Faksimile_075.pdf
DOI: 10.25625/xnsxv6/eg31yb
title: 20180516_1108_K562_5EU_1440_labeled_III_run.tar.gz

description: Human K562 cells labeled with 5EU for 24 h, replicate no. 3 (5EU 24 h III, nano-ID fast5 files).
DOI: 10.7483/opendata.cms.tzsd.me69
title: /MET/Run2012C-22Jan2013-v1/AOD
DOI: 10.34664/1575664
title: 380 GeV e+e- collisions, SM type, PYTHIA6 generator: gev380ee_pythia6_zpole_tautau

description: PYTHIA version 6.4. Z production (Zpole) with decays to tau (tau+tau-). Other details in the log file.