cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/kn4r-ar77
title: Coronavirus (COVID-19) Global Sentiment Trend

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple positive peaks and negative peaks that need to be further studied. The n-grams on those peaking and dropping periods can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format.
DOI: 10.21227/b6vb-5125
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. 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 coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. 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.-------------------------------------Recently added dataset: Coronavirus (COVID-19) Global Sentiment Trend-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 235,960 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.5519/qd.fvi1fzph
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (118 records)
DOI: 10.6084/m9.figshare.13042946
title: Revisión Sistemática de Aprendizaje Autorregulado en estudiantes de las Ciencias de la Salud

description: Material de repositorio del artículo Revisión Sistemática de Aprendizaje Autorregulado en estudiantes de las Ciencias de la Salud publicado en la Revista Educación Médica Superior http://www.ems.sld.cu/index.php/ems<br>Se expone la Matriz de Análisis y las tablas de los resultados de la investigación.
DOI: 10.6084/m9.figshare.13042946.v2
title: Revisión Sistemática de Aprendizaje Autorregulado en estudiantes de las Ciencias de la Salud

description: Material de repositorio del artículo Revisión Sistemática de Aprendizaje Autorregulado en estudiantes de las Ciencias de la Salud publicado en la Revista Educación Médica Superior http://www.ems.sld.cu/index.php/ems<br>Se expone la Matriz de Análisis y las tablas de los resultados de la investigación.
DOI: 10.6084/m9.figshare.13042946.v1
title: Revisión Sistemática de Aprendizaje Autorregulado en estudiantes de las Ciencias de la Salud

description: Se expone la Matriz de Análisis y las tablas de los resultados de la investigación
DOI: 10.6084/m9.figshare.12915422.v1
title: PercepCovid19OriginSocialdist.SurveyMonkey.Data.xlsx

description: Dataset from the study: "Misinformation on COVID-19 origin and its relationship with perception and knowledge about social distancing: A cross-sectional study".<br><br><br>Background: The media, in an attempt to strengthen the awareness of COVID-19 among the public, have instead created more controversies, especially regarding the origin of the virus. To date, very little, if any information exists regarding the perceptions about the origin of COVID-19 and the knowledge and perception about social distancing among the populace of the Dominican Republic.Objective: The study aimed at ascertaining the knowledge and perception about social distancing and its relationship with individuals’ perception about the origin of COVID-19.Methods: A web-based cross-sectional study was conducted among five provinces of the Dominican Republic population within the period of June to July of 2020. The data collection instrument exploited in the study was a self-designed questionnaire using SurveyMonkeyTM, distributed throughout different social media platforms. The collected data was exported into an excel spreadsheet (Microsoft Excel 365®, 2016) for data cleaning and analyzed using SPSS software (IBM SPSS® version 21).Results: A total of 1195 respondents successfully completed the online questionnaire survey. Exactly 597 (50.0%) were males whilst the remaining 598 (50%) were females. The level of education was significantly associated (P = .0.017) with individuals’ perception about the origin of COVID-19, whilst only age (P = .032) and education level (P &lt; .001) statistically significantly predicted ‘knowledge about social distancing’. Perception of COVID-19 origin was statistically significant associated (P =
DOI: 10.5281/zenodo.4063477
title: Wikidata Dump testdump

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4063476
title: Wikidata Dump testdump

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.34740/kaggle/dsv/1532133
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5517/ccdc.csd.cc1pjr87
title: CCDC 1565229: Experimental Crystal Structure Determination

description: Related Article: Andrii Vakulka, Evgeny Goreshnik|2018|J.Coord.Chem.|71|2426|doi:10.1080/00958972.2018.1481210
DOI: 10.5517/ccdc.csd.cc1pjrb9
title: CCDC 1565231: Experimental Crystal Structure Determination

description: Related Article: Andrii Vakulka, Evgeny Goreshnik|2018|J.Coord.Chem.|71|2426|doi:10.1080/00958972.2018.1481210
DOI: 10.5517/ccdc.csd.cc1pkc4r
title: CCDC 1565814: Experimental Crystal Structure Determination

description: Related Article: Christina Römelt, Thomas Weyhermüller, Karl Wieghardt|2019|Coord.Chem.Rev.|380|287|doi:10.1016/j.ccr.2018.09.018
description: Related Article: Mei Wang, Christina Römelt, Thomas Weyhermüller, Karl Wieghardt|2019|Inorg.Chem.|58|121|doi:10.1021/acs.inorgchem.8b01949
DOI: 10.5517/ccdc.csd.cc1pl7xf
title: CCDC 1566675: Experimental Crystal Structure Determination

description: Related Article:  R. Alan Aitken, Lewis Davidson, Alexandra M. Z. Slawin|2020|J.Chem.Cryst.|50|8|doi:10.1007/s10870-018-0748-9
DOI: 10.5517/ccdc.csd.cc1plzbl
title: CCDC 1567370: Experimental Crystal Structure Determination

description: Related Article: Danh X. Ngo, Wesley W. Kramer, Brendon J. McNicholas, Harry B. Gray, Bradley J. Brennan|2019|Inorg.Chem.|58|737|doi:10.1021/acs.inorgchem.8b02862
DOI: 10.5517/ccdc.csd.cc1plzcm
title: CCDC 1567371: Experimental Crystal Structure Determination

description: Related Article: Danh X. Ngo, Wesley W. Kramer, Brendon J. McNicholas, Harry B. Gray, Bradley J. Brennan|2019|Inorg.Chem.|58|737|doi:10.1021/acs.inorgchem.8b02862
DOI: 10.5517/ccdc.csd.cc1plzdn
title: CCDC 1567372: Experimental Crystal Structure Determination

description: Related Article: Danh X. Ngo, Wesley W. Kramer, Brendon J. McNicholas, Harry B. Gray, Bradley J. Brennan|2019|Inorg.Chem.|58|737|doi:10.1021/acs.inorgchem.8b02862
DOI: 10.5066/p9ewhayw
title: Worksheet for computing annual exceedance probability flood discharges and prediction intervals at stream sites in Connecticut

description: The U.S. Geological Survey (USGS), in cooperation with Connecticut Department of Transportation, completed a study to improve flood-frequency estimates in Connecticut. This companion data release is a Microsoft Excel workbook for: (1) computing flood discharges for the 50- to 0.2-percent annual exceedance probabilities from peak-flow regression equations, and (2) computing additional prediction intervals, not available through the USGS StreamStats web application. The current StreamStats application (version 4) only computes the 90-percent prediction interval for stream sites in Connecticut. The Excel workbook can be used to compute the 70-, 80-, 90-, 95-, and 99-percent prediction intervals. The prediction interval provides upper and lower limits of the estimated flood discharge with a certain probability, or level of confidence in the accuracy of the estimate. The standard error of prediction for the Connecticut peak-flow regression equations ranged from 26.3 to 45.0 percent (Ahearn and Hodgkins, 2020). The Excel workbook consists of four worksheets. The worksheets provide an overview of how the application works; input and output tables of the explanatory variables and flood discharges, and graphical display of the results; and the computational formulas used to estimate the flood discharges and prediction intervals.
DOI: 10.5281/zenodo.4064077
title: ANON usability evaluation data

description: Additional data related to usability evaluation of the ANON system.
DOI: 10.5281/zenodo.4064076
title: ANON usability evaluation data

description: Additional data related to usability evaluation of the ANON system.
DOI: 10.21227/10by-h123
title: Optimal feature configuration for malware detection datasets

description: Datasets consiting of features extracted from API calls, network activity and Cuckoo signatures of 19994 public files of different types.
DOI: 10.21227/vvax-v881
title: Optimal feature configuration for malware detection datasets

description: Datasets consiting of features extracted from API calls, network activity and Cuckoo signatures of 19994 public files of different types.
DOI: 10.21227/7wxn-n828
title: Annotated image dataset of household objects from the RoboFEI@Home team

description: Annotated image dataset of household objects from the RoboFEI@Home teamThis data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.The data set contains the basic resources for the creation of custom object detection systems. Users can use the provided annotated images to train their own models and validate them in a set of test images, or they can analyze the inference time of the proposed methods in the provided videos.Example videos of detection systems trained with this data set can be found in the following links:https://www.youtube.com/watch?v=Py6_qG52EYQhttps://www.youtube.com/watch?v=fXJYmJOaSxQ
DOI: 10.21227/c89v-2s42
title: Annotated image dataset of household objects from the RoboFEI@Home team

description: Annotated image dataset of household objects from the RoboFEI@Home teamThis data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.The data set contains the basic resources for the creation of custom object detection systems. Users can use the provided annotated images to train their own models and validate them in a set of test images, or they can analyze the inference time of the proposed methods in the provided videos.Example videos of detection systems trained with this data set can be found in the following links:https://www.youtube.com/watch?v=Py6_qG52EYQhttps://www.youtube.com/watch?v=fXJYmJOaSxQ
DOI: 10.6073/pasta/fd547b8d19b948f04def63b81dd13f9a
title: Porewater nutrient concentrations from control plots at a Spartina patens-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.

description: Porewater samples from a Spartina patens-dominated salt marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site were analyzed for ammonium, phosphate, sulfide and chloride concentrations.
DOI: 10.5281/zenodo.4064113
title: A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)

description: The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.
description: Datasets for the journal article "A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)" in CSV format. Column names in all files as follows (abbreviation and unit, if any): - datetime (dd/mm/yyyy HH:MM), - year (yyyy), - month (mm), - month (mm), - day (dd), - hour (HH), - min (MM), - sec, - T_ext (outdoor temperature, in °C), - RH_ext (outdoor relative humidity, in %), - T1 (indoor temperature, in °C), - RH1 (indoor relative humidity, in %), - T2 (close to wall temperature, in °C), - RH2 (close to wall relative humidity, in %), - Ts2 (wall surface temperature, in °C), - WD (air flow direction, in °), - Ws_mean (air flow intensity, in m/s), - Ws_max (maximum air flow intensity, in m/s).
DOI: 10.5281/zenodo.4064112
title: A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)

description: The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.
description: Datasets for the journal article "A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)" in CSV format. Column names in all files as follows (abbreviation and unit, if any): - datetime (dd/mm/yyyy HH:MM), - year (yyyy), - month (mm), - month (mm), - day (dd), - hour (HH), - min (MM), - sec, - T_ext (outdoor temperature, in °C), - RH_ext (outdoor relative humidity, in %), - T1 (indoor temperature, in °C), - RH1 (indoor relative humidity, in %), - T2 (close to wall temperature, in °C), - RH2 (close to wall relative humidity, in %), - Ts2 (wall surface temperature, in °C), - WD (air flow direction, in °), - Ws_mean (air flow intensity, in m/s), - Ws_max (maximum air flow intensity, in m/s).
DOI: 10.25416/edgehill.9116990
title: Diatom counts and stratigraphic analyses from Moss Lake, Washington, USA

description: Data has been obtained from lake sediment cores from Moss Lake, Washington. Refer to Egan et al., 2016 to specific location details. Two cores were taken, one from the centre (MLC) and one from the fringe (MLF).<br>Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research<br>Collection period: 20 May 2013 - 31 January 2015<br>Additional information:Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research
DOI: 10.25416/edgehill.9116990.v2
title: Diatom counts and stratigraphic analyses from Moss Lake, Washington, USA

description: Data has been obtained from lake sediment cores from Moss Lake, Washington. Refer to Egan et al., 2016 to specific location details. Two cores were taken, one from the centre (MLC) and one from the fringe (MLF).<br>Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research<br>Collection period: 20 May 2013 - 31 January 2015<br>Additional information:Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research
DOI: 10.25416/edgehill.9116990.v1
title: Diatom counts and stratigraphic analyses from Moss Lake, Washington, USA

description: Data has been obtained from lake sediment cores from Moss Lake, Washington. Refer to Egan et al., 2016 to specific location details. Two cores were taken, one from the centre (MLC) and one from the fringe (MLF).<br>Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research<br>Collection period: 20 May 2013 - 31 January 2015<br>Additional information:Egan, J et al. (submitted): Diatom inferred aquatic impacts of the mid-Holocene eruption of Mount Mazama, Oregon. Quaternary Research
DOI: 10.6073/pasta/c7741987c66336b43028b50eab0524fc
title: Porewater nutrient concentrations from control plots at a Spartina alterniflora-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.

description: Porewater samples from a Spartina alterniflora-dominated salt marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site were analyzed for ammonium, phosphate, sulfide and chloride concentrations.
DOI: 10.6084/m9.figshare.13043519
title: Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis

description: Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis
DOI: 10.6084/m9.figshare.13043519.v1
title: Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis

description: Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis
DOI: 10.21227/drg6-ad92
title: Annotated image dataset of household objects from the RoboFEI@Home team

description: Annotated image dataset of household objects from the RoboFEI@Home teamThis data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.The data set contains the basic resources for the creation of custom object detection systems. Users can use the provided annotated images to train their own models and validate them in a set of test images, or they can analyze the inference time of the proposed methods in the provided videos.Example videos of detection systems trained with this data set can be found in the following links:https://www.youtube.com/watch?v=Py6_qG52EYQhttps://www.youtube.com/watch?v=fXJYmJOaSxQ
DOI: 10.5517/ccdc.csd.cc1pn2xb
title: CCDC 1568442: Experimental Crystal Structure Determination

description: Related Article: Yuhang Yao, Hao-Yan Yin, Yingying Ning, Jian Wang, Yin-Shan Meng, Xinyue Huang, Wenkai Zhang, Lei Kang, Jun-Long Zhang|2019|Inorg.Chem.|58|1806|doi:10.1021/acs.inorgchem.8b02376
DOI: 10.5517/ccdc.csd.cc1pn33k
title: CCDC 1568448: Experimental Crystal Structure Determination

description: Related Article: Yuhang Yao, Hao-Yan Yin, Yingying Ning, Jian Wang, Yin-Shan Meng, Xinyue Huang, Wenkai Zhang, Lei Kang, Jun-Long Zhang|2019|Inorg.Chem.|58|1806|doi:10.1021/acs.inorgchem.8b02376
DOI: 10.5517/ccdc.csd.cc1pn34l
title: CCDC 1568449: Experimental Crystal Structure Determination

description: Related Article: Yuhang Yao, Hao-Yan Yin, Yingying Ning, Jian Wang, Yin-Shan Meng, Xinyue Huang, Wenkai Zhang, Lei Kang, Jun-Long Zhang|2019|Inorg.Chem.|58|1806|doi:10.1021/acs.inorgchem.8b02376
DOI: 10.5517/ccdc.csd.cc1pn2yc
title: CCDC 1568443: Experimental Crystal Structure Determination

description: Related Article: Yuhang Yao, Hao-Yan Yin, Yingying Ning, Jian Wang, Yin-Shan Meng, Xinyue Huang, Wenkai Zhang, Lei Kang, Jun-Long Zhang|2019|Inorg.Chem.|58|1806|doi:10.1021/acs.inorgchem.8b02376
DOI: 10.5517/ccdc.csd.cc1pn37p
title: CCDC 1568452: Experimental Crystal Structure Determination

description: Related Article: Yuhang Yao, Hao-Yan Yin, Yingying Ning, Jian Wang, Yin-Shan Meng, Xinyue Huang, Wenkai Zhang, Lei Kang, Jun-Long Zhang|2019|Inorg.Chem.|58|1806|doi:10.1021/acs.inorgchem.8b02376
DOI: 10.5517/ccdc.csd.cc1pn38q
title: CCDC 1568453: Experimental Crystal Structure Determination

description: Related Article: Sakkani Nagaraju, Kota Sathish, Neeli Satyanarayana, Banoth Paplal, Dhurke Kashinath|2020|J.Heterocycl.Chem.|57|469|doi:10.1002/jhet.3803
DOI: 10.5517/ccdc.csd.cc1pt8kb
title: CCDC 1573422: Experimental Crystal Structure Determination

description: Related Article: Shan Zhu, Huiping Hu, Jiugang Hu, Jiyuan Li, Fang Hu, YongxI Wang|2018|J.Coord.Chem.|71|2441|doi:10.1080/00958972.2018.1482416