cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3840001
title: CL-Windcon high fidelity SOWFA simulations dataset

description: This document presents a description and a guideline for using the CL-Windcon project high fidelity SOWFA computations data sets. CL-Windcon project (Closed Loop Wind Farm Control) is a H2020 EU funded projects that brings up with new innovative ways of focusing the design and operation of wind farms, as it tries to put light into the wind farm closed-loop control paradigm, enhancing the understanding and modelling of the flow dynamics inside the wind farm, in order to simultaneously improve the efficiency and reliability and therefore reducing the cost of energy. More information about the project can be found at https://www.clwindcon.eu . This SOWFA dataset contains the computations carried out with SOWFA within the CL-Windcon project for creating a public database that could be useful to adjust the wakes of the engineering models. The dataset is stored at an FTP repository owned by CENER. The data set is open access under requirement. To get access to the data, please send an email to clwindconftp@cener.com with the title "Cl-Windcon FTP data access request".
DOI: 10.21227/j7z8-fj31
title: mmWave Radar face signatures

description: This database includes data measured by Qualcomm's 60GHz mmWave Radar.It includes:Face signature data base: radar face scan data of 206 individuals for face recognition.
DOI: 10.5281/zenodo.3889750
title: NN5 Weekly Dataset

description: This is the aggregated version of the daily dataset used in the NN5 forecasting competition. It contains 111 weekly time series from the banking domain. The goal is predicting the weekly cash withdrawals from ATMs in UK. The original dataset contains missing values. A missing value on a particular day is replaced by the median across all the same days of the week along the whole series and then, have been aggregated into weekly.
description: {"references": ["Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012. A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Systems with Applications 39(8), 7067 - 7083.", "Neural Forecasting Competitions, 2008. NN5 forecasting competition for artificial neural networks and computational intelligence. Accessed: 2020-05-10. URL http://www.neural-forecasting-competition.com/NN5/"]}
DOI: 10.21227/y0eh-2a95
title: mmWave Radar face signatures

description: This database includes data measured by Qualcomm's 60GHz mmWave Radar.It includes:Face signature data base: radar face scan data of 206 individuals for face recognition.
DOI: 10.21227/9a2d-pc94
title: mmWave Radar face signatures

description: This database includes data measured by Qualcomm's 60GHz mmWave Radar.It includes:Face signature data base: radar face scan data of 206 individuals for face recognition.
DOI: 10.21227/tvkm-c961
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”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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 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 (above 170+ 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-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 it private. And if the same tweet has already been pulled and is being shared on a public domain, then 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: 10.21227/cgvt-zp03
title: EEG signals dataset

description: EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.
DOI: 10.21227/qeyp-jt17
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”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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 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 (above 170+ 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-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 it private. 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: 10.21227/t5rz-g722
title: EEG Signal Dataset

description: EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.
DOI: 10.21227/k4qn-ye05
title: EEG Signal Dataset

description: EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.
DOI: 10.21227/ympg-f275
title: A deep learning database and network for focusing guided wave defect detection

description: Database set information
DOI: 10.21227/x29n-xv81
title: A deep learning database and network for focusing guided wave defect detection

description: Database set information
DOI: 10.5281/zenodo.3889786
title: Traffic Hourly Dataset

description: This dataset contains the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 hourly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.
description: {"references": ["Caltrans, 2020. Caltrans performance measurement system, california department of transportation. Accessed: 2020-04-30. URL http://pems.dot.ca.gov/", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889806
title: Traffic Weekly Dataset

description: This dataset contains an aggregated version of the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 weekly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.
description: {"references": ["Caltrans, 2020. Caltrans performance measurement system, california department of transportation. Accessed: 2020-04-30. URL http://pems.dot.ca.gov/", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3828602
title: Introducing assassin snails (Anentome helena) as a biological model for exploring multi-prey feeding behaviour of generalist predators

description: Data and R code for Berkhout & Morozov 2020.; Introducing assassin snails (Anentome helena) as a biological model for exploring multi-prey feeding behaviour of generalist predators. All files needed to run the analyses in Berkhout & Morozov 2020.<br> Questions can be directed at bwberkhout[at]protonmail.com<br> Updates will be published on www.boriswberkhout.com <br> data folder: contains file with raw data<br> scripts folder: contains helper scripts<br> Data analylis...Rmd Analysis script to be run in Rstudio<br> Data analysis...pdf Pdf produced from Rmd file for convenient checking of the code
DOI: 10.5281/zenodo.3889830
title: Electricity Hourly Dataset

description: The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014. The uploaded dataset is an aggregated version of the original dataset used by Lai et al. (2017). It contains 321 hourly time series from 2012 to 2014.
description: {"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889840
title: Bioplastic Perception Survey

description: Conducted at the beginning of a Bioplastics and Online Citizen Science webinar on zoom, which was a public event advertised through social media platforms. The questions are based on understanding what citizens know about discarding bioplastic materials and what happens to those materials after they discard it. These answers to these questions will prompt the discussions and activities we should engage in.
DOI: 10.5281/zenodo.3889839
title: Bioplastic Perception Survey

description: Conducted at the beginning of a Bioplastics and Online Citizen Science webinar on zoom, which was a public event advertised through social media platforms. The questions are based on understanding what citizens know about discarding bioplastic materials and what happens to those materials after they discard it. These answers to these questions will prompt the discussions and activities we should engage in.
DOI: 10.5281/zenodo.3889846
title: Measuring personality traits in Eurasian red squirrels: a critical comparison of different methods

description: Data and R code used for the analysis of Eurasian red squirrels (<em>Sciurus vulgaris</em>) personality traits derived from open field test (activity, shyness, exploration) and capture-mark-recapture data (trappability/trap-diversity).
DOI: 10.5281/zenodo.3889845
title: Measuring personality traits in Eurasian red squirrels: a critical comparison of different methods

description: Data and R code used for the analysis of Eurasian red squirrels (<em>Sciurus vulgaris</em>) personality traits derived from open field test (activity, shyness, exploration) and capture-mark-recapture data (trappability/trap-diversity).
DOI: 10.5281/zenodo.3889881
title: CLDF dataset derived from Tang and Her's "Quantitative typological data on classifiers and plural markers" from 2019

description: Cite the source of the dataset as: Tang, Marc and One-Soon Her. 2019. Insights on the Greenberg-Sanches-Slobin Generalization: Quantitative typological data on classifiers and plural markers. Folia Linguistica, 53(2): 297-331. https://doi.org/10.1515/flin-2019-2013
DOI: 10.5281/zenodo.3889880
title: CLDF dataset derived from Tang and Her's "Quantitative typological data on classifiers and plural markers" from 2019

description: Cite the source of the dataset as: Tang, Marc and One-Soon Her. 2019. Insights on the Greenberg-Sanches-Slobin Generalization: Quantitative typological data on classifiers and plural markers. Folia Linguistica, 53(2): 297-331. https://doi.org/10.1515/flin-2019-2013
DOI: 10.5287/bodleian:x59egb0pv
title: Data acquired to demonstrate improved qBOLD measurements of oxygen extraction fraction using hyperoxia-BOLD derived measures of blood volume.
DOI: 10.5281/zenodo.3889935
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 14

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889934
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 14

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.25592/uhhfdm.733
title: 3D-model of a plaster cast of so called Dornauszieher/Spinario

description: 3D-model of a plaster cast of the Dornauszieher / Spinario https://www.fundus.uni-hamburg.de/collection_records/63516 Gipsabguss-Sammlung des Archäologischen Instituts der Universität Hamburg Inv.: 110 ID: 16 Forming shop : Formerei at Rom, Virgilio Gherardi Dimensions: hight: 73 cm / With: 50 cm / Depth: 64 cm Date: Greek original: 3. B.C.; roman copy: 1. B.C. This 3D scan was made possible within the framework of the Hamburg Open Science programme by funding from the Ministry of Science, Research and Equality of the Free and Hanseatic City of Hamburg.
DOI: 10.25592/uhhfdm.732
title: 3D-model of a plaster cast of so called Dornauszieher/Spinario

description: 3D-model of a plaster cast of the Dornauszieher / Spinario https://www.fundus.uni-hamburg.de/collection_records/63516 Gipsabguss-Sammlung des Archäologischen Instituts der Universität Hamburg Inv.: 110 ID: 16 Forming shop : Formerei at Rom, Virgilio Gherardi Dimensions: hight: 73 cm / With: 50 cm / Depth: 64 cm Date: Greek original: 3. B.C.; roman copy: 1. B.C. This 3D scan was made possible within the framework of the Hamburg Open Science programme by funding from the Ministry of Science, Research and Equality of the Free and Hanseatic City of Hamburg.
DOI: 10.21227/29p5-a821
title: COVID-19 tweets dataset for Bengali language

description: This dataset is very vast and contains Bengali tweets related to COVID-19. There are 36117 unique tweet-ids in the whole dataset that ranges from December 2019 till May 2020 . The keywords that have been used to crawl the tweets are 'corona', , 'covid ' , 'sarscov2 ', 'covid19', 'coronavirus '. For getting the other 33 fields of data drop a mail at "avishekgarain@gmail.com". Code snippet is given in Documentation file. Sharing Twitter data other than Tweet ids publicly violates Twitter regulation policies.
DOI: 10.21227/26yn-za64
title: COVID-19 tweets dataset for Spanish language

description: This dataset is very vast and contains Spanish tweets related to COVID-19. There are 18958 unique tweet-ids in the whole dataset that ranges from December 2019 till May 2020 . The keywords that have been used to crawl the tweets are 'corona',,'covid ' , 'sarscov2 ','covid19', 'coronavirus '. For getting the other 33 fields of data drop a mail at "avishekgarain@gmail.com". Code snippet is given in Documentation file. Sharing Twitter data other than Tweet ids publicly violates Twitter regulation policies.
DOI: 10.5281/zenodo.3889939
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 15

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889938
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 15

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889941
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 16

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889940
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 16

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889945
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 17

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889944
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 17

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889949
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 18

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889948
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 18

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889955
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 19

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889954
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 19

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889963
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 20

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889962
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 20

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889967
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 21

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889966
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 21

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889953
title: Electricity Weekly Dataset

description: The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014. The uploaded dataset is an aggregated version of the hourly electricity dataset used by Lai et al. (2017). It contains 321 weekly time series from 2012 to 2014.
description: {"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.3889969
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 22

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889968
title: PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 22

description: These data were collected from FIDIA machine tool controller during milling operation.
DOI: 10.5281/zenodo.3889970
title: Data and Code Accompanying the Study "Developing an annotation framework for word formation processes in comparative linguistics"

description: Cite the source of the dataset as: Schweikhard, N. E. and List, J.-M. (2020): Developing an annotation framework for word formation processes in comparative linguistics. SKASE Journal of Theoretical Linguistics. 17.1: 2-26.
DOI: 10.5281/zenodo.3888237
title: Data and Code Accompanying the Study "Developing an annotation framework for word formation processes in comparative linguistics"

description: Cite the source of the dataset as: Schweikhard, N. E. and List, J.-M. (2020): Developing an annotation framework for word formation processes in comparative linguistics. SKASE Journal of Theoretical Linguistics. 17.1: 2-26.
DOI: 10.5281/zenodo.3889972
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 14

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889971
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 14

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889979
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 15

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889978
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 15

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889983
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 16

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889982
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 16

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889985
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 17

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889984
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 17

description: These data were collected from robot controller during solar tanks welding operation.
DOI: 10.5281/zenodo.3889989
title: PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 18

description: These data were collected from robot controller during solar tanks welding operation.