cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/k3rjmzkm44.3
title: Supplementary Material
DOI: 10.17632/t5tngb9kr7.1
title: Semantic Scholar Evaluation

description: The dataset is constructed for a project that investigates the coverage and the role of Semantic Scholar (S2) search engine in condunting secondary studies in software engineering.
DOI: 10.5281/zenodo.3876703
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.3876713
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: 10.17632/2rzz7xfwkv.1
title: Zebrafish Purkinje cells electrical properties

description: Title Functionally-distinct Purkinje cell types show temporal precision in encoding locomotion Authors Weipang Chang, Andrea Pedroni, Victoria Hohendorf, Stefania Giacomello, Masahiko Hibi, Reinhard W. Köster and Konstantinos Ampatzis Abstract Purkinje cells, the principal neurons of cerebellar computations, are believed to comprise a uniform neuronal population of cells, each with similar functional properties. Here, we show an undiscovered heterogeneity of adult zebrafish Purkinje cells, revealing the existence of anatomically and functionally distinct cell types. Dual patch-clamp recordings showed that the cerebellar circuit contains all different Purkinje cell types that cross-communicate extensively using chemical and electrical synapses. Further activation of spinal central pattern generators (CPGs) revealed unique phase-locked activity from each Purkinje cell type during the locomotor cycle. Thus, we show intricately organized Purkinje cell networks in the adult zebrafish cerebellum that encode the locomotion rhythm differentially, and we suggest that these organizational properties may also apply to other cerebellar functions.
DOI: 10.17632/vm2gy2w56s.1
title: caixas

description: dataset de caixas
DOI: 10.21227/dhbm-mz61
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.[In the thumbnail: "Earth+mask" is an illustration by cromaconceptovisual]
DOI: 10.17632/4yx789kvn9.1
title: Replication experiments and microstructure changes of the ancient co-fusion steelmaking process

description: Microstructure of co-fusion samples
DOI: 10.21227/xsd0-c051
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.[In the thumbnail: "Earth+mask" is an illustration by cromaconceptovisual]
DOI: 10.17632/5xvvv682hs.1
title: Data for article "Simplistic correlations between molecular electronic properties and inhibition efficiencies: Do they really exist?"

description: This dataset contains raw/processed data required to reproduce findings of the manuscript entitled »Simplistic correlations between molecular electronic properties and inhibition efficiencies: Do they really exist?« by Anton Kokalj et al. submitted to Corrosion Science. In particular, it contains experimental data of linear polarization resistance measurements (all Rp values) and input and output files of molecular DFT calculations, performed with Gaussian-16. With the provided data, the findings presented in the manuscript can be reproduced. The zip file unzips into the folder: Kokalj_et_al_Data. Please read the file README.txt therein for further description of files.
DOI: 10.17632/kkmpk5k4pr.1
title: Vainio_2020_Sound symbolism in vowel production

description: Includes data of the study for Experiment 1, 2 and 3.
DOI: 10.17632/n52h7d4gk2.1
title: Suplementary Data Lopes et al

description: Raw data of the manuscript "Reduction of bacteria in relation to feeding regimes when treating aquaculture waste in fly larvae composting", regarding larval growth and process parameters, and daily inactivation of microorganisms. With these raw data, anyone can calculate larval growth parameters, process performance parameters and the inactivation of microorganisms, using the described equations and steps in the manuscript.
DOI: 10.5281/zenodo.3876187
title: Raw diffraction data for [NiFeSe] hydrogenase G491A variant pressurized with O2 gas - dataset G491A-O2-HD

description: Diffraction data measured at ESRF beamline ID30B on April 8, 2018. Image files are uploaded in blocks of gzip-compressed cbf files.
description: This work was supported by grant PTDC/BBB-BEP/2885/2014 funded by Fundação para a Ciência e Tecnologia (FCT/MCTES), and by research unit MOSTMICRO (Project LISBOA-01-0145-FEDER-007660) cofunded by FCT/MCTES and FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI). SZ was recipient of fellowship SFRH/BD/100314/2014 from FCT/MCTES.
DOI: 10.5281/zenodo.3876188
title: Raw diffraction data for [NiFeSe] hydrogenase G491A variant pressurized with O2 gas - dataset G491A-O2-HD

description: Diffraction data measured at ESRF beamline ID30B on April 8, 2018. Image files are uploaded in blocks of gzip-compressed cbf files.
description: This work was supported by grant PTDC/BBB-BEP/2885/2014 funded by Fundação para a Ciência e Tecnologia (FCT/MCTES), and by research unit MOSTMICRO (Project LISBOA-01-0145-FEDER-007660) cofunded by FCT/MCTES and FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI). SZ was recipient of fellowship SFRH/BD/100314/2014 from FCT/MCTES.
DOI: 10.15468/efsvn4
title: The species of Campodorus F?rster, 1869 and a related species (Hymenoptera, Ichneumonidae) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sun, Shu-Ping, Li, Tao, Sheng, Mao-Ling, L?, Jun (2020): The species of Campodorus F?rster, 1869 and a related species (Hymenoptera, Ichneumonidae) from China. European Journal of Taxonomy 658: 1-26, DOI: https://doi.org/10.5852/ejt.2020.658
DOI: 10.17632/hrmyfhpb5c.1
title: Generic boundaries in subtribe Saussureinae (Compositae-Cardueae): insights from Hyb-Seq data
DOI: 10.17632/hrmyfhpb5c.2
title: Generic boundaries in subtribe Saussureinae (Compositae-Cardueae): insights from Hyb-Seq data
DOI: 10.17632/jbbs52642f.1
title: Kmiecik et al: Hsp70s dissociate Hsf1 trimers from DNA by unzipping the trimerization domain

description: Original blots for Figures 2BC, 5D, 5F, S5, and S6
DOI: 10.17632/7n69yppg79.1
title: The interplay between loneliness, mindfulness and presence of meaning. Does the search for meaning matter?

description: The aim of the study was to investigate the interplay between loneliness and mindfulness in predicting presence of meaning (POM ), taking into consideration the additional role of the search for meaning (SFM). A sample of 415 participants from Poland aged 18 - 55 (M = 27.88; SD = 8.66) completed a set of 3 questionnaires: the De Jong-Gierveld Loneliness Scale, the Mindful Attention and Awareness Scale, and Meaning in Life Questionnaire. Our results suggest that mindfulness partially mediated the relationship between loneliness and POM. This effect was moderated by SFM. Specifically, an indirect effect was found among participants with medium- and high-levels of search for meaning but not in the participants with a low level of this variable. Furthermore, it turned out that SFM enhanced the relationship between mindfulness and POM. These results are discussed in the context of the evolutionary theory of loneliness, meaning-making mechanisms of mindfulness and schema-like properties of SFM.
DOI: 10.17632/fjbdvys93z.1
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/k3rjmzkm44.1
title: Supplementary Material
DOI: 10.17632/k3rjmzkm44.2
title: Supplementary Material
DOI: 10.5282/ubm/data.190
title: Predicted protein sequences from metagenomes and metatranscriptomes from subseafloor deep sea clay, at three separate locations in the North Atlantic
DOI: 10.21227/nyt0-xb17
title: Irradiance to Illuminance Conversion

description: The standard for measurement of solar irradiance utilizes the units of watts per meter squared (W/m2). Irradiance meters are both costly and limited in the ability to measure low irradiance values. With a lower cost and higher sensitivity in low light conditions, light meters measure luminous flux per unit area (illuminance) utilizing the units of lumens per meter squared or lux (lx). An effective conversion factor between W/m2 and lx would enable the use of light meters to evaluate photovoltaic performance under low solar irradiance conditions. A survey of the literature found no definitive and readily available "rule of thumb" conversion standard between solar irradiance and illuminance. Easy-to-find Internet sources contain conflicting and widely varying values of ranging from 688449 to 21000 lx for 1000 W/m2 (1 Sun) of solar irradiance. Peer-reviewed literature contains Luminous Efficacy equivalent values ranging from 21 to 131 lx per W/m2. This manuscript explores the relationship and establishes a theoretical and laboratory measurement guide for the conversion between solar irradiance and illuminance. The conversion factor includes standard data, and equipment calibration accuracy and uncertainty estimates. Solar Irradiance of 1 Sun (1000 W/m2) for an LED-based solar simulator is (116 ± 3) klx and (122 ± 1) klx for outdoor sunlight.
DOI: 10.21227/tfng-y787
title: Irradiance to Illuminance Conversion

description: The standard for measurement of solar irradiance utilizes the units of watts per meter squared (W/m2). Irradiance meters are both costly and limited in the ability to measure low irradiance values. With a lower cost and higher sensitivity in low light conditions, light meters measure luminous flux per unit area (illuminance) utilizing the units of lumens per meter squared or lux (lx). An effective conversion factor between W/m2 and lx would enable the use of light meters to evaluate photovoltaic performance under low solar irradiance conditions. A survey of the literature found no definitive and readily available "rule of thumb" conversion standard between solar irradiance and illuminance. Easy-to-find Internet sources contain conflicting and widely varying values of ranging from 688449 to 21000 lx for 1000 W/m2 (1 Sun) of solar irradiance. Peer-reviewed literature contains Luminous Efficacy equivalent values ranging from 21 to 131 lx per W/m2. This manuscript explores the relationship and establishes a theoretical and laboratory measurement guide for the conversion between solar irradiance and illuminance. The conversion factor includes standard data, and equipment calibration accuracy and uncertainty estimates. Solar Irradiance of 1 Sun (1000 W/m2) for an LED-based solar simulator is (116 ± 3) klx and (122 ± 1) klx for outdoor sunlight.
DOI: 10.21227/270e-wv88
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: 10.5281/zenodo.3877206
title: IO Islamic 319. Tafsîr-i-tadhkirat-alanbiyâ wa alumam, A Dogmatic History of the Prophets from Âdam to Muḥammad

description: IO Islamic 319. Tafsîr-i-tadhkirat-alanbiyâ wa alumam, A Dogmatic History of the Prophets from Âdam to Muḥammad
DOI: 10.5281/zenodo.3877207
title: IO Islamic 319. Tafsîr-i-tadhkirat-alanbiyâ wa alumam, A Dogmatic History of the Prophets from Âdam to Muḥammad

description: IO Islamic 319. Tafsîr-i-tadhkirat-alanbiyâ wa alumam, A Dogmatic History of the Prophets from Âdam to Muḥammad
DOI: 10.5281/zenodo.3876871
title: Dataset Primula palinuri

description: Dataset of vitality test and capsule dehiscence of Primula palinuri
DOI: 10.21227/ekjd-ad35
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: 10.5281/zenodo.3877212
title: IO Islamic 595. Jawâmi'-alḥikâyât wa lawâmi'-alriwâyât, A Complete Copy of the Famous Collection of Stories and Anecdotes

description: IO Islamic 595. Jawâmi’-alḥikâyât wa lawâmi’-alriwâyât, A Complete Copy of the Famous Collection of Stories and Anecdotes
DOI: 10.5281/zenodo.3877213
title: IO Islamic 595. Jawâmi'-alḥikâyât wa lawâmi'-alriwâyât, A Complete Copy of the Famous Collection of Stories and Anecdotes

description: IO Islamic 595. Jawâmi’-alḥikâyât wa lawâmi’-alriwâyât, A Complete Copy of the Famous Collection of Stories and Anecdotes
DOI: 10.15129/dc789acf-704d-42ea-bfd6-9b5dd02c9417
title: Data for: "Optical characterisation of micro-fabricated Fresnel holograms for atomic waveguides"

description: This is the data associated with the figures in the paper "Optical characterisation of micro-fabricated Fresnel holograms for atomic waveguides", particularly the experimental images that were obtained in the focal region of the binary Fresnel zone plates.
DOI: 10.5281/zenodo.3874421
title: 1-km daily average air temperature of the Tibetan Plateau (1980-2014)

description: 1) Data content (including elements and meanings): Gridded daily average air temperature of the Tibetan Plateau during 1980-2014 at 1-km resolution 2) Data source and processing method: Developed by integrating 8 types of reanalysis data (i.e., NNRP-2, 20CRV2c, JRA-55, ERA-Interim, MERRA2, CFSR, GLDAS and ERA5) downscaled with MODIS-estimated temperature lapse rates based on machine learing 3) Data quality description: According to leave-one-out validation based on stations, the average RMSE at China Adimistration Stations is about 1.7 ℃ and that at high-elevation field stations is about 1.9 ℃ 4) Data application results and prospects: This dataset can be used as air temperature input for driving long-term hydrologial modelling or evaluated for use in climate analysis 5) Recommended Citation: Zhang, H., Zhang, F., Zhang, G., Che, T., & Yan, W. (2018). How accurately can the air temperature lapse rate over the Tibetan Plateau be estimated from MODIS LSTs?. Journal of Geophysical Research: Atmospheres, 123(8), 3943-3960.
DOI: 10.5281/zenodo.3874422
title: 1-km daily average air temperature of the Tibetan Plateau (1980-2014)

description: 1) Data content (including elements and meanings): Gridded daily average air temperature of the Tibetan Plateau during 1980-2014 at 1-km resolution 2) Data source and processing method: Developed by integrating 8 types of reanalysis data (i.e., NNRP-2, 20CRV2c, JRA-55, ERA-Interim, MERRA2, CFSR, GLDAS and ERA5) downscaled with MODIS-estimated temperature lapse rates based on machine learing 3) Data quality description: According to leave-one-out validation based on stations, the average RMSE at China Adimistration Stations is about 1.7 ℃ and that at high-elevation field stations is about 1.9 ℃ 4) Data application results and prospects: This dataset can be used as air temperature input for driving long-term hydrologial modelling or evaluated for use in climate analysis 5) Recommended Citation: Zhang, H., Zhang, F., Zhang, G., Che, T., & Yan, W. (2018). How accurately can the air temperature lapse rate over the Tibetan Plateau be estimated from MODIS LSTs?. Journal of Geophysical Research: Atmospheres, 123(8), 3943-3960.