cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/hxb7-8310
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". 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).Note: I started sharing the IDs of the tweets that contained the exact 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 (above 150+ million tweets) 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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.25919/5ed87e16c0b17
title: Parkes observations for project P1021 semester 2020APRS_02

description: We propose to continue our observations of PSR J1653-45, a 951-ms pulsar in a long, 1.5-yr orbit. Binary pulsars are valuable objects of scientific study, allowing for a wide range of applications, including tests of gravity, probes of the neutron star equation of state, and fossil records of stellar evolution. Long spin-period pulsars in binary systems are generally much rarer than faster-spinning `recycled’ pulsars, and represent an under-explored region of pulsar binary evolution. In addition to its long orbital period, PSR J1653-45 also experiences extended eclipses, lasting up to half of the orbital period and whose nature is not well understood. These eclipses have also prevented the determination of an orbital or phase-connected timing solution, both vital steps in allowing for the continued study of this system. We propose a targeted, high-cadence observing campaign designed to monitor this pulsar as it transitions out of its eclipsing state. The campaign has the twin goals of both monitoring how the eclipse affects the pulsar’s emission properties, thereby revealed more about the nature of the eclipses themselves, and of extracting as much information regarding the pulsar’s orbit during its least-explored regions, thereby constrained and hopefully solving the pulsar’s orbit and ideally leading to the development of a timing solution. In both instances, we hope to gain a greater understanding of this pulsar’s properties and how it fits into this under-explored class of unrecycled pulsar binaries.
DOI: 10.21227/jzk5-7453
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". 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).Note: I started sharing the IDs of the tweets that contained the exact 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 (above 150+ million tweets) 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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.[Image credits: "Earth+mask" illustration by cromaconceptovisual & "Twitter bird" vector by Marlon Romanelli]
DOI: 10.5281/zenodo.3874943
title: Dataset Collected by JSObserver

description: This is a sampled dataset collected by JSObserver on Alexa top 100K websites. We analyze the log files to identify JavaScript global identifier conflicts, i.e., variable value conflicts, variable type conflicts and function definition conflicts. We release the log files on websites where we detect the above conflicts, and split the whole dataset into 10 subsets, i.e., <em>1-50K-0.zip</em> ~ <em>50K-100K-4.zip</em>. The writes to a memory location in JavaScript are saved in [rank].[main/sub].[frame_cnt].asg (e.g., 1.main.0.asg) files. JavaScript global function definitions are saved in [rank].[main/sub].[frame_cnt].func (e.g., 1.main.0.func) files. The maps from script IDs to script URLs are saved in [rank].[main/sub].[frame_cnt].id2url (e.g., 1.main.0.id2url) files. The source code of scripts are saved in [rank].[main/sub].[frame_cnt].[script_ID].script (e.g., 1.main.0.17.script) files. We also sample 100 websites on which we did not detect any conflicts. The log files collected on those websites are available in <em>sampled_no_conflict.zip</em>
DOI: 10.5281/zenodo.3874944
title: Dataset Collected by JSObserver

description: This is a sampled dataset collected by JSObserver on Alexa top 100K websites. We analyze the log files to identify JavaScript global identifier conflicts, i.e., variable value conflicts, variable type conflicts and function definition conflicts. We release the log files on websites where we detect the above conflicts, and split the whole dataset into 10 subsets, i.e., <em>1-50K-0.zip</em> ~ <em>50K-100K-4.zip</em>. The writes to a memory location in JavaScript are saved in [rank].[main/sub].[frame_cnt].asg (e.g., 1.main.0.asg) files. JavaScript global function definitions are saved in [rank].[main/sub].[frame_cnt].func (e.g., 1.main.0.func) files. The maps from script IDs to script URLs are saved in [rank].[main/sub].[frame_cnt].id2url (e.g., 1.main.0.id2url) files. The source code of scripts are saved in [rank].[main/sub].[frame_cnt].[script_ID].script (e.g., 1.main.0.17.script) files. We also sample 100 websites on which we did not detect any conflicts. The log files collected on those websites are available in <em>sampled_no_conflict.zip</em>
DOI: 10.17632/33m7sc3wnc.1
title: Bariatric surgery in obese patients with end-stage heart failure with ventricular assist devices

description: This is the raw data captured for a systematic review and individual participant data (IPD) meta-analysis evaluating the trends in body mass indices of obese patients with end-stage heart failure (ESHF) with ventricular assist devices (VADs) after undergoing bariatric surgery (BS). A systematic search was performed in ClinicalTrials.gov, Cochrane, Embase and PubMed on November 23rd, 2019. Additional searches were conducted in Google Scholar, websites of the most important journals for ESHF and BS, which also contained the proceedings of the most relevant scientific meetings in such specialties: -The Journal of Heart and Lung Transplantation -Journal of Cardiac Failure, Obesity Surgery -Surgery for Obesity and Related Diseases -Journals of following groups (journal families): --Journal of the American College of Cardiology --Circulation --European Heart Journal These searches were performed at different before April 20th, 2020. Multiple official indexing terms and additional relevant terms for VADs and BS were used. Studies were selected if they reported IPD for the postoperative BMI of VAD patients after undergoing BS. Only patients with VAD support at the time of BS were included in the study. References selection, and data extraction were performed independently and in parallel by two investigators. In cases of disagreements, consensus was attempted through discussion, but if that was not achieved, a third investigator helped to solve the tie by simple majority. This database contains the most relevant variables from an ESHF and BS stand point. More details about data description and analyses can be found in the meta-analysis and data publication related to this project.
DOI: 10.17632/33m7sc3wnc.2
title: Bariatric surgery in obese patients with end-stage heart failure with ventricular assist devices

description: This is the raw data captured for a systematic review and individual participant data (IPD) meta-analysis evaluating the trends in body mass indices of obese patients with end-stage heart failure (ESHF) with ventricular assist devices (VADs) after undergoing bariatric surgery (BS). A systematic search was performed in ClinicalTrials.gov, Cochrane, Embase and PubMed on November 23rd, 2019. Additional searches were conducted in Google Scholar, websites of the most important journals for ESHF and BS, which also contained the proceedings of the most relevant scientific meetings in such specialties: -The Journal of Heart and Lung Transplantation -Journal of Cardiac Failure, Obesity Surgery -Surgery for Obesity and Related Diseases -Journals of following groups (journal families): --Journal of the American College of Cardiology --Circulation --European Heart Journal These searches were performed at different before April 20th, 2020. Multiple official indexing terms and additional relevant terms for VADs and BS were used. Studies were selected if they reported IPD for the postoperative BMI of VAD patients after undergoing BS. Only patients with VAD support at the time of BS were included in the study. References selection, and data extraction were performed independently and in parallel by two investigators. In cases of disagreements, consensus was attempted through discussion, but if that was not achieved, a third investigator helped to solve the tie by simple majority. This database contains the most relevant variables from an ESHF and BS stand point. More details about data description and analyses can be found in the meta-analysis and data publication related to this project.
DOI: 10.17632/rtpmybkpdy.1
title: Collective Dissipative Structures, Force Flow Reciprocity, and the Foundations of Perception Action Mutuality. Benjamin De Bari, James A. Dixon, Dilip Kondepudi, & Bruce Kay

description: This paper explored the coordination-like phenomena observed in collective dissipative structures. We observed the coordination of two self-organized electrical structures that oscillated together in a shared electrical field. The system is called the Electrical Self-Organized Foraging Implementation (E-SOFI). The structures are coupled by this field and settle into stable oscillatory regimes defined by their relative phase. We also observed the oscillatory dynamics of simulated electrical structures, using a computational model called the Charge Depletion Model (CDM; De Bari et al., 2019). We predicted that both in-phase (zero radians relative phase) and anti-phase (pi radians relative phase) would be stable oscillatory modes for the physical and simulated system. Previous work has demonstrated that this system self-selects for states of maximal current. We predicted that the model would demonstrate differential stability of in-phase and anti-phase modes, in accordance with whichever mode produced the greater current. This data set includes the digitally tracked positions of the physical structures, gathered from video data. One data set demonstrated in-phase coordination, and another demonstrated anti-phase coordination. Also included are corresponding data from simulations of the physical model. Simulations tested the existence of the in-phase and anti-phase modes, and the current conducted during the coordination. Simulations were conducted across a parameter space that varied the stability of the in- and anti-phase coordination modes. We demonstrated stability in the in-phase coordination mode, and potential meta-stability of the anti-phase mode in the physical system. Simulations suggested that within a reasonable parameter space the anti-phase mode can be made stable. Simulations also revealed that within a given parameter space, whichever mode produces greater current is also more stable. An R Markdown file includes the code to compute the relative phase of both the physical and simulated data, as well as the code used to produce the plots in the published manuscript.
DOI: 10.17632/9bddfnc7wb.1
title: A Structure-Based Mechanism for DNA Entry into the Cohesin Ring. Higashi et al.

description: Original data set
DOI: 10.17632/gbxfpzgtsk.2
title: Raw data of the article: Effects of Different Feeding Frequency on Siamese Fighting Fish (Betta splenden) and Guppy (Poecilia reticulata) Juveniles: Data on Growth Performance and Survival Rate

description: Raw data on weight and length measurements, fish count and feed intake of Betta and Guppy during 60 days different feeding frequency trials
DOI: 10.17632/vj24br7gnt.1
title: Major and trace elements in sediments and soils of the Zerafshon area, north - west Tajikistan

description: The data set contains 8816 numerical values regarding the content of 9 major elements as oxides: SiO2, TiO2, Al2O3, FeO, MnO2, MgO, CaO, K2O, and Na2O (in %) and 29 trace elements: Sc, V, Cr, Co, Ni, Zn, As, Br, Rb, Sr, Zr, Sb, Cs, Ba, La, Ce, Nd, Sm, Eu, Gd, Tb, Tm, Yb, Hf, Ta, W, Hg, Th, and U (in mg/kg) as determined by Epithermal Neutron Activation Analysis in 116 samples of sediments and soils, corresponding to 116 different sampling points at an altitude between 883 and 3629 m.
DOI: 10.17632/wssjnsmftw.1
title: Title: From short-term surgical missions towards sustainable partnerships A survey among active members in the global surgical workforce

description: answers of 62 participants of a questionnaire
DOI: 10.5281/zenodo.3833940
title: A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios

description: Binaural room impulse responses (BRIRs) and spherical microphone array impulse responses (DRIRs) of the WDR broadcast studios in Cologne in SOFA file format. The BRIRs were measured with a Neumann KU100 dummy head for 360 directions along the horizontal plane (1° spatial resolution). The DRIRs were measured for rigid- and open-sphere configurations on different Lebedev grids. For further details, please refer to the conference publications listed below: Stade, P. Bernschütz, B. Rühl, M. "A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios" (2012) Proceedings of the 27th Tonmeistertagung - VDT International Convention Bernschütz, B. Stade, P. Rühl, M. "Sound Field Analysis in Room Acoustics" (2012) Proceedings of the 27th Tonmeistertagung - VDT International Convention ________________________________________________________________________________________________________ <strong>Contact:</strong><br> Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany<br> https://www.th-koeln.de/akustik _________________________________________________________________ <strong>Naming Convention (please see name_coding_chart.pdf for more details):</strong> <strong>LBS</strong>: Large Broadcast Studio (Großer Sendesaal / Klaus-von-Bismarck-Saal) <strong>SBS:</strong> Small Broadcast Studio (Kleiner Sendesaal) <strong>CR1: </strong>Control Room 1 <strong>CR7</strong>: Control Room 7 <strong>KU: </strong>Neumann KU100 Dummy Head <strong>VSA</strong>: VariSphear Microphone Array <strong>MIC</strong>: (Stereo) Microphones
description: {"references": ["Stade, P. Bernsch\u00fctz, B. R\u00fchl, M. \"A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios\" (2012) Proceedings of the 27th Tonmeistertagung - VDT International Convention", "Bernsch\u00fctz, B. Stade, P. R\u00fchl, M. \"Sound Field Analysis in Room Acoustics\" (2012) Proceedings of the 27th Tonmeistertagung - VDT International Convention"]}
DOI: 10.5281/zenodo.3865305
title: The role of perception and action on the use of allocentric information in a large-scale virtual environment

description: Final allo weights, filtered as explained in the manuscript
DOI: 10.5281/zenodo.3865306
title: The role of perception and action on the use of allocentric information in a large-scale virtual environment

description: Final allo weights, filtered as explained in the manuscript
DOI: 10.5281/zenodo.3876704
title: Deep_Statistical_Solvers_-_AC_Power_Flow_14

description: Dataset for the training and testing of Deep Statistical Solvers AC power flow problem on the IEEE case14 power grid and snapshots from the L2RPN competition
DOI: 10.5281/zenodo.3876714
title: Spherical harmonic model of the magnetic field of Mars from Langlais et al. (2019)

description: <strong>Langlais2019.sh.gz</strong> is a gzipped file of the magnetic potential coefficients of Mars as published by Langlais et al. (2019). This is the same as the file jgre21147-sup-0003-Table_SI-S01.txt in the supporting information of this manuscript.
description: {"references": ["Langlais, B., Th\u00e9bault, E., Houliez, A., Purucker, M.E., Lillis, R.J. (2019). A new model of the crustal magnetic field of Mars using MGS and MAVEN. Journal of Geophysical Research: Planets, 124, 1542-1569, doi:10.1029/2018JE005854."]}
DOI: 10.15161/oar.it/23708
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.17632/8cy7v679zv.3
title: IMAPI (Early Childhood Development Friendly Index) dataset
DOI: 10.17632/d6ybdxzkwz.1
title: A Reinforcement-Learning Model of Active Avoidance in Wistar-Kyoto and Sprague Dawley Rats: Experimental Dataset with Model Code and Output

description: The purpose of this experiment was to compare between-strain differences regarding avoidance behavior. Active avoidance data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during a foot-shock experiment. The experiment consisted of 12 sessions each composed of 25 trials. Each trial consisted of a warning period, a possible shock period (if avoidance did not occur), and an intertrial interval (ITI) representing a safety period. During warning periods, rats had an option to press a lever in order to avoid future shock. However, if a lever press did not occur, then a shock period began during which the rats could press the lever to escape the foot-shock. After an escape or an avoidance, a 180-second safe period began (ITI). The maximum time of a warning period and a shock were both 72-seconds each if the rat failed to lever press during both periods. Every session also began with a 60-second habituation period during which the rat could familiarize itself with the experimental cage. Lever presses during ITI periods were classified as inter-trial responses (ITRs) whereas lever presses during the habituation periods were classified as anticipatory responses (ARs). Lever presses during shock periods were labeled as escapes “E” whereas lever presses during warning periods were labeled as avoidances “A”. Lever press data were discretized to 12-second periods. Frequency counts for lever presses during each 12-second timestep can be found in the raw data files (located in the “ratRL_datafiles_Model_InputOutput” folder). An example name of this file is “S09.csv” representing an SD rat or “W09.csv” representing a WKY rat. A reinforcement learning, actor-critic model was applied to this raw data to determine different learning parameters for each rat. The code for this model fitting can be found in the folder titled “ratRL_ModelFitting_Code”. Based on learning parameters determined from the reinforcement learning model code, a simulation of theoretical rats running through the experimental protocol was created. This simulation can be found in the “ratRL_Simulation_Code” folder. Inputs to this simulation are found in the “ratRL_codedTrials_Sim_InputOutput” folder. The summaries for each experimental rat’s performance during the foot-shock avoidance experiment are labeled “Sum” files (ie S09sum.csv) and the learning parameters for each rat are stored in the file called “parm_listfile.csv”. Output files are labeled “out” (ie S09out.csv) and represent the performance of a theoretical simulated rat. The differences in learning parameters between the two rat strains can provide insight into anxiety-disorders as the WKY rats have been used as an animal model for anxiety. Learning parameters can also be mapped to certain brain regions in order to explore possible neurological differences in the two strains in future experiments.
DOI: 10.17632/dtkjs2rmvb.1
title: Supplementary Table. Select Transition of Care Programs

description: Supplementary Table. Highlights from transition of care (TOC) programs: Select pediatric to adult TOC programs from articles written between 2009 and 2019 in the United States and Canada that discussed specific program features.
DOI: 10.17632/fjbdvys93z.2
title: Supplementary Material, Appendix A

description: Supplementary Material, Appendix A. Objectively measured sedentary time and physical activity levels are associated with neck adipose tissue volume in young, sedentary adults
DOI: 10.17632/ggyfxz9ky8.1
title: Database of the Manuscript: Depressive symptoms and affective disorders during pregnancy in women from low socioeconomic level in Argentina

description: One dataset is shared. The data file named “Database Depressive symptoms” contains the maternal characteristics and outcomes variables and it is structured with one record per woman. The data includes variables that were constructed in order to run the analysis. The data dictionary of the database is also included and named “Data Dictionary Depressive symptoms”.