cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4004958
title: Cause-Effect-Context from Natural Questions (NQ-CE)

description: This dataset is derived from the Natural Questions (NQ) dataset which is a large benchmark for open question answering research (https://ai.google.com/research/NaturalQuestions). This dataset contains a collection of cause-effect pairs along with their context (the text describing the causal relation between the cause and the effect) as well as the original question in the NQ data set. It also contains a collection of "negative" pairs, phrases that are mentioned in the context but have no causal relation. This dataset is constructed by filtering questions in the NQ dataset that follow a certain pattern indicating that the question is causal. Either the cause or the effect is the (short) answer in the original NQ dataset, and the other side is manually derived from the context. The data is shared in JSONL format with every line being a processed NQ question with relevant fields described above. In version 2, each JSON object has the following fields: phrase1: the first phrase (text span) phrase2: the second phrase (text span) label: "causal" means phrase 1 causes phrase 2, "non_causal" means "phrase1" and "phrase2" do NOT have a causal relation between them passage: the context that states that phrase1 causes phrase2 (for causal) or just the passage that has both phrase1 and phrase2 (for non_causal). document_url: the Wikipedia URL from the Natural Questions data question_text: the original question text from the Natural Questions data License: https://creativecommons.org/licenses/by-sa/3.0/ Contacts:<br> Gaurav Dass: dassg2 AT rpi.edu<br> Oktie Hassanazadeh
DOI: 10.3886/e113367v2-81240
title: Replication data for: Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments

description: We consider estimation of and inference about coefficients on endogenous variables in a linear instrumental variables model where the number of instruments and exogenous control variables are each allowed to be larger than the sample size. We work within an approximately sparse framework that maintains that the signal available in the instruments and control variables may be effectively captured by a small number of the available variables. We provide a LASSO-based method for this setting which provides uniformly valid inference about the coefficients on endogenous variables. We illustrate the method through an application to demand estimation.
DOI: 10.21227/whey-sd13
title: January-18-Basu-Sukanta

description: January-18-Basu-Sukanta
DOI: 10.5255/ukda-sn-854723
title: Social Influence and Disruptive Low Carbon Innovations, 2019

description: These two datasets were collected as part of the SILCI project (‘Social influence and disruptive low carbon innovations’). The SILCI project ran from 2016 - 2021 at the Tyndall Centre for Climate Change Research, University of East Anglia and was funded by the European Research Council (ERC) through the Starting Grant #678799. Further details on the SILCI project and related publications can be found at: http://www.silci.org. The SILCI project explored disruptive low carbon innovations and how they spread through processes of social influence. As part of the SILCI project, a national online survey was conducted in both the UK and Canada to understand consumers' perceptions, communication behaviour, and adoption propensity towards a wide range of low-carbon innovations in four different consumer domains: transport, food, homes and energy. The survey instrument was developed and tested by the project team. The survey was implemented by an international market research company in the UK between 2nd July – 3rd September 2019 and in Canada between 11th October – 14th November 2019. A total of n=3014 responses were collected in the UK and n=3352 in Canada. The survey responses were coded and cleaned by the project team. Both the survey instrument and cleaned response data are made available here.
DOI: 10.25678/0003m5
title: Net Ecosystem Production

description: This study presents a novel concept for estimating net ecosystem production (NEP), the export of organic carbon (OC) from the productive surface layer to the deep-water (hypolimnion) of eleven seasonally stratified lakes, varying in depth and trophic state. As oxygen remineralizes settling OC at a constant ratio, NEP is equivalent to the areal hypolimnetic mineralization rate (AHM) plus burial in the sediment (net sedimentation, NS). Two major interferences have to be considered, however. First, OC from terrestrial sources, not originating from primary production, consumes a fraction of oxidants. Second, sediment diagenetic processes of lakes in trophic transition (e.g. undergoing eutrophication or reoligotrophication) that are not in quasi-steady-state with actual fluxes of OC in the productive surface layer, bias the estimation of NEP. In these cases, we suggest subtracting the flux of reduced substances diffusing from the sediment. This results in some overestimation for lakes with high allochthonous loads, and slight underestimation in lakes that are not in quasi-steady-state, because the fraction of the actual sediment burial of autochthonous OC is small but not negligible. The presented approach requires data from routinely available chemical monitoring and thus can be applied to historic data. The seasonal time integration makes the estimation of NEP quite robust. Exemplary, NEP of Lake Geneva was estimated from the export of P and N from the productive zone during the summer season to the hypolimnion assembling seasonal budgets. Based on a historic data record of 47 years, NEP estimations from AHM rates agreed well with P and N budgets and helped to verify and constrain the uncertainty of the estimates.
DOI: 10.5255/ukda-sn-854451
title: COVID-19 Impact Dataset: Great British Intelligence Test, 2020

description: There is an urgent need to understand the factors that mediate and mitigate the impact of the Covid-19 pandemic on behaviour and wellbeing. However, the onset of the outbreak was unexpected and the rate of acceleration so rapid as to preclude the planning of studies that can address these critical issues. Coincidentally, in January 2020, just prior to the outbreak in the UK, my team launched a study that collected detailed (~50 minute) cognitive and questionnaire assessments from &gt;200,000 members of the UK public as part of a collaboration with the BBC. This placed us in a unique position to examine how aspects of mental health subsequently changed as the pandemic arrived in the UK. Therefore, we collected data from a further ~120,000 people in May, including additional detailed measures of self-perceived pandemic impact and free text descriptions of the main positives, negatives and pragmatic measures that people found helped them maintain their wellbeing. In this data archive, we include the survey data from January and May 2020 examining impact of Covid-19 on mood, wellbeing and behaviour in the UK population. This data is reported in a preprint article, where we apply a novel fusion of psychometric, multivariate and machine learning analyses to this unique dataset, in order to address some of the most pressing questions regarding wellbeing during the pandemic in a data-driven manner. The preprint is available on this URL. https://www.medrxiv.org/content/10.1101/2020.06.18.20134635v1
DOI: 10.25345/c5fr4n
title: MassIVE MSV000087051 - PROTEOMIC ANALYSIS OF THE UMBILICAL CORD IN FETAL GROWTH RESTRICTION AND PREECLAMPSIA
title: Fetal growth restriction &#40;FGR&#41; is associated with adverse perinatal outcomes. Pre-eclampsia &#40;PreE&#41; increases the associated perinatal morbidity and mortality. The structure of the umbilical cord in the setting of FGR and PreE has yet to be determined. This study aimed to examine changes in the umbilical cord &#40;UC&#41; composition in pregnancies complicated by FGR and FGR with PreE. UC from gestational age-matched pregnancies with isolated FGR &#40;n &#61; 5&#41;, FGR with PreE &#40;n &#61; 5&#41; and controls and controls &#40;n &#61; 5&#41; were collected, and a portion of the UC was processed for histologic and proteomic analysis. Manual segmentation analysis was performed to measure cross-section areas of umbilical cord regions. Wharton&#39;s Jelly samples were analyzed on a tims-TOF Pro. Spectral count and ion abundance data were analyzed simultaneously, creating an intersection dataset from a combination of multiple mass spectrometry search and inference engines. UCs from FGR and FGR with PreE had lower cross-sectional area and Wharton&#39;s Jelly area compared with control &#40;p &#61 ;0.03&#41;. When comparing isolated FGR to control, 28 proteins were significantly different in abundance analysis and 34 in spectral count analysis &#40;p-value &#60; 0.05&#41;. Differential expression analysis between PreE with FGR vs controls, demonstrated 48 proteins were significantly different in abundance and 5 in spectral count &#40;p-value &#60; 0.05&#41;. The majority of proteomic changes occur in proteins associated with extracellular matrix, cellular process, inflammatory, and angiogenesis pathways.
DOI: 10.23679/506
title: Results of chemical analysis of whole rock samples from Jõhvi magnetic anomaly
DOI: 10.6084/m9.figshare.14036528.v2
title: Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario

description: These represent aggregate case and test counts, and population estimates, for Ontario, Canada during the 2019-2021 COVID-19 pandemic. The data include tests and cases from March 1, 2020 to December 8, 2020. Data are aggregated at the daily, weekly, seasonal, and overall levels. The first page of the dataset contains a data dictionary.
DOI: 10.6084/m9.figshare.14036528
title: Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario

description: These represent aggregate case and test counts, and population estimates, for Ontario, Canada during the 2019-2021 COVID-19 pandemic. The data include tests and cases from March 1, 2020 to December 8, 2020. Data are aggregated at the daily, weekly, seasonal, and overall levels. The first page of the dataset contains a data dictionary.
DOI: 10.6084/m9.figshare.14036528.v3
title: Data for Fisman et al., Test-Adjusted Incidence of COVID-19 in Ontario

description: These represent aggregate case and test counts, and population estimates, for Ontario, Canada during the 2019-2021 COVID-19 pandemic. The data include tests and cases from March 1, 2020 to December 8, 2020. Data are aggregated at the daily, weekly, seasonal, and overall levels. The first page of the dataset contains a data dictionary.
DOI: 10.6084/m9.figshare.14217080
title: 19502120.fastq.gz

description: WGS of canine stool microbiome
DOI: 10.6084/m9.figshare.14217080.v1
title: 19502120.fastq.gz

description: WGS of canine stool microbiome
DOI: 10.5281/zenodo.4606078
title: BurnCare Physiology Data: Burn without Intervention

description: A standard male patient with burns of 10%, 15%, 20%, 30%, and 40% TBSA were simulated through 24 hours without intervention. Note, the patient with 30% TBSA or greater burn did not survive the full simulation (hence the smaller data set). The newest version also includes a log and csv results file for each simulation. Additionally, state files for the golden path of resuscitation have been added with their corresponding csv result files. The 10xTBSA golden path starts the resuscitation pathway with fluids being given at a rate of 10xTBSA (then titrated up or down each hour based on urine output). The half dose initial rate golden path bases its starting rate of fluids on half the daily dose given over the first eight hours (then titrated up or down each hour based on urine output).
description: {"references": ["https://biogearsengine.com/"]}
DOI: 10.5066/p9t0csct
title: Pesticide detections in streams throughout the foothills of the Sierra Nevada range using passive samplers from 2017 to 2019

description: This dataset was produced from analyzing Affinisep SDB-RPS, Affinisep HLB, and Restek Resprep C18 disks that were deployed with the use of Chemcatcher? passive sampling devices. Silicone bands were also deployed and used for exploring economical methods of sampling for pesticides in surface water. Sampling assemblies were deployed in streams throughout the foothills of the Sierra Nevada range and California's northern Central Valley from 2017 to 2019. Samples were extracted in situ then eluted and concentrated at the Organic Chemistry Research Laboratory in Sacramento, CA. Samples from silicone bands were extracted via agitation in appropriate solvents. Samples were analyzed for a suite of 155 pesticides and degradates with gas chromatography-tandem mass spectrometry and liquid chromatography-tandem mass spectrometry. There were a total of 21 different pesticides detected throughout this study.
DOI: 10.5519/qd.4239ohpe
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (455021 records)
DOI: 10.6084/m9.figshare.13664606
title: CrillyCooper2021.xlsx

description: transcriptomic analysis of innate immune cells after brain haemorrhage in a zebrafish larval model
DOI: 10.5517/ccdc.csd.cc1lsfrk
title: CCDC 1483249: Experimental Crystal Structure Determination

description: Related Article: Riccardo Suter, Robert J. Gilliard, Javad Iskandarov, Zoltan Benkő, Michael Wörle, Hansjörg Grützmacher|2021|Inorg.Chim.Acta|520|120274|doi:10.1016/j.ica.2021.120274
DOI: 10.5517/ccdc.csd.cc1lgtnj
title: CCDC 1474008: Experimental Crystal Structure Determination

description: Related Article: Riccardo Suter, Robert J. Gilliard, Javad Iskandarov, Zoltan Benkő, Michael Wörle, Hansjörg Grützmacher|2021|Inorg.Chim.Acta|520|120274|doi:10.1016/j.ica.2021.120274
DOI: 10.5517/ccdc.csd.cc1ly1h2
title: CCDC 1487674: Experimental Crystal Structure Determination

description: Related Article: Riccardo Suter, Robert J. Gilliard, Javad Iskandarov, Zoltan Benkő, Michael Wörle, Hansjörg Grützmacher|2021|Inorg.Chim.Acta|520|120274|doi:10.1016/j.ica.2021.120274
DOI: 10.5517/ccdc.csd.cc25hb45
title: CCDC 2010726: Experimental Crystal Structure Determination

description: Related Article: Fa-Lei Zhang, Chen Shi, Li-Tang Sun, Hui-Xiang Yang, Juan He, Zheng-Hui Li, Tao Feng, Ji-Kai Liu|2021|Phytochemistry|183|112625|doi:10.1016/j.phytochem.2020.112625
DOI: 10.5281/zenodo.4589065
title: Political Polarization on Twitter

description: This is the training data used to produce the results shown in the paper listed below. Source: Sampled public tweets from Twitter streaming API. Date range: 6 weeks prior to the 2010 Congressional midterm elections. Contains: Three networks of political communication between Twitter users Please cite: Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Alessandro Flammini, and Filippo Menczer. Political Polarization on Twitter. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
DOI: 10.5281/zenodo.4589064
title: Political Polarization on Twitter

description: This is the training data used to produce the results shown in the paper listed below. Source: Sampled public tweets from Twitter streaming API. Date range: 6 weeks prior to the 2010 Congressional midterm elections. Contains: Three networks of political communication between Twitter users Please cite: Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Alessandro Flammini, and Filippo Menczer. Political Polarization on Twitter. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
DOI: 10.5281/zenodo.4589063
title: Astroturf/Legitimate Classification

description: This is the training data used to produce the results shown in the paper listed below. Source: Sampled public tweets from Twitter streaming API. Date range: September 14 to October 27, 2010. Contains: <strong>data.arff</strong>: holds the un-resampled training data. <strong>data_balanced.arff</strong>: holds the resampled training data. <strong>data.instance_to_id.pickle</strong>: holds a Python pickle relating instance IDs in the data.arff file with Meme IDs in the Truthy database. To view the page for a particular meme ID, go to http://truthy.indiana.edu/m?id= Please cite: Jacob Ratkiewicz, Michael Conover, Mark Meiss, Bruno Goncalves, Alessandro Flammini, and Filippo Menczer. Detecting and Tracking Political Abuse in Social Media. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
DOI: 10.5281/zenodo.4589062
title: Astroturf/Legitimate Classification

description: This is the training data used to produce the results shown in the paper listed below. Source: Sampled public tweets from Twitter streaming API. Date range: September 14 to October 27, 2010. Contains: <strong>data.arff</strong>: holds the un-resampled training data. <strong>data_balanced.arff</strong>: holds the resampled training data. <strong>data.instance_to_id.pickle</strong>: holds a Python pickle relating instance IDs in the data.arff file with Meme IDs in the Truthy database. To view the page for a particular meme ID, go to http://truthy.indiana.edu/m?id= Please cite: Jacob Ratkiewicz, Michael Conover, Mark Meiss, Bruno Goncalves, Alessandro Flammini, and Filippo Menczer. Detecting and Tracking Political Abuse in Social Media. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
DOI: 10.6084/m9.figshare.14219258
title: participant_data.tsv

description: Anonymized participant data - The Effect Of Crowding On The Reading Of Program Code For Programmers With Dyslexia
DOI: 10.5281/zenodo.4489036
title: Topical diversity of user interests and content

description: Source: Sampled public tweets from Twitter streaming API. Date range: January 1, 2013 to March 31, 2013. Data size: 6.4 GB; about 490 millions tweets. Contains: Sampled tweets during 3 months. Each tweet is associated with a timestamp, anonymized user ID, and a list of hashtags. Please cite: Lilian Weng and Filippo Menczer. Topicality and Social Impact: Diverse Messages but Focused Messengers. Under review. 2014.
DOI: 10.5281/zenodo.4487270
title: Social Bookmarking Dataset

description: A collection of bookmarks from GiveALink.org for the month of November 2009. Source: GiveALink.org. Date range: November 1, 2009 to November 30, 2009. File size: 61,665 posts (approximately 430,000 triples); 12MB uncompressed Please cite: Ben Markines, Lubomira Stoilova, and Filippo Menczer. Bookmark hierarchies and collaborative recommendation. In Proc. 21st National Conference on Artificial Intelligence (AAAI), pp 1375-1380, 2006. Lubomira Stoilova, Todd Holloway, Ben Markines, Ana G. aguitman, and Filippo Menczer. GiveALink: Mining a Semantic Network of Bookmarks for Web Search and Recommendation. In Proc. KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), 2006.
DOI: 10.5281/zenodo.4487269
title: Social Bookmarking Dataset

description: A collection of bookmarks from GiveALink.org for the month of November 2009. Source: GiveALink.org. Date range: November 1, 2009 to November 30, 2009. File size: 61,665 posts (approximately 430,000 triples); 12MB uncompressed Please cite: Ben Markines, Lubomira Stoilova, and Filippo Menczer. Bookmark hierarchies and collaborative recommendation. In Proc. 21st National Conference on Artificial Intelligence (AAAI), pp 1375-1380, 2006. Lubomira Stoilova, Todd Holloway, Ben Markines, Ana G. aguitman, and Filippo Menczer. GiveALink: Mining a Semantic Network of Bookmarks for Web Search and Recommendation. In Proc. KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), 2006.
DOI: 10.5281/zenodo.4589071
title: Last.fm Dataset

description: This is the data used to produce the results shown in the paper below. Source: A crawl of Last.fm users, their annotations, friends and neighborhood relations, and group membership. Date range: First half of 2009. Please cite: Schifanella, R., Barrat, A., Cattuto, C., Markines, B., and Menczer, F. (2010). Folks in Folksonomies: Social Link Prediction from Shared Metadata. Proc. 3rd ACM International Conference on Web Search and Data Mining (WSDM). arXiv Preprint
DOI: 10.5281/zenodo.4589070
title: Last.fm Dataset

description: This is the data used to produce the results shown in the paper below. Source: A crawl of Last.fm users, their annotations, friends and neighborhood relations, and group membership. Date range: First half of 2009. Please cite: Schifanella, R., Barrat, A., Cattuto, C., Markines, B., and Menczer, F. (2010). Folks in Folksonomies: Social Link Prediction from Shared Metadata. Proc. 3rd ACM International Conference on Web Search and Data Mining (WSDM). arXiv Preprint
DOI: 10.5281/zenodo.4487154
title: Fact Checking Dataset

description: DBpedia 2016-10 knowledge graph in a format used by Knowledge Linker (KL), Relational Knowledge Linker (KL-REL) and Knowledge Stream (KS) algorithms, and a collection of synthetic and real datasets used to evalute them and other state-of-the-art methods applicable to fact checking. Some of the synthetic datasets were created by us, while others were downloaded from KGMiner. Source: DBpedia 2016 File size: 538M compressed; ~3G uncompressed Please cite: Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, Giovanni Luca Ciampaglia Finding Streams in Knowledge Graphs to Support Fact Checking. In Proc. of the Intl. Conf. on Data Mining, 2017.
DOI: 10.5281/zenodo.4487153
title: Fact Checking Dataset

description: DBpedia 2016-10 knowledge graph in a format used by Knowledge Linker (KL), Relational Knowledge Linker (KL-REL) and Knowledge Stream (KS) algorithms, and a collection of synthetic and real datasets used to evalute them and other state-of-the-art methods applicable to fact checking. Some of the synthetic datasets were created by us, while others were downloaded from KGMiner. Source: DBpedia 2016 File size: 538M compressed; ~3G uncompressed Please cite: Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, Giovanni Luca Ciampaglia Finding Streams in Knowledge Graphs to Support Fact Checking. In Proc. of the Intl. Conf. on Data Mining, 2017.
DOI: 10.5281/zenodo.4589048
title: Prediction of Viral Memes on Twitter

description: Source: Sampled public tweets from Twitter streaming API. Date range: March 24, 2012 to April 25, 2012. Data size: 178 MB Contains: Anonymized reciprocal follower/retweet/mention network of the same group of users; The sequence of adopters and timestamps for observed hashtags. Please cite: Lilian Weng, Filippo Menczer, and Yong-Yeol Ahn. Virality Prediction and Community Structure in Social Networks. Nature Scientific Report. (3)2522, 2013. Lilian Weng, Filippo Menczer, and Yong-Yeol Ahn. Predicting Meme Virality in Social Networks using Network and Community Structure. Proc. 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM). 2014.
DOI: 10.6084/m9.figshare.14043513.v1
title: sessions

description: For each participant, full recording session data file as generated by the Tobii Pro Lab export feature in tsv format.
DOI: 10.6084/m9.figshare.14043513
title: sessions

description: For each participant, full recording session data file as generated by the Tobii Pro Lab export feature in tsv format.
DOI: 10.5281/zenodo.4604513
title: Estimation of Snow Cover Area using multi-sensor data

description: This dataset is part of the experiments carried out to test our methodology on the estimation of snow cover area based on the synergistic use of MODIS and Sentinel-1 data. Different parameters such as the dual polarimetric entropy, mean scattering angle, backscatter coefficients, and the interferometric coherence, are integrated with a spatially resampled Normalized Difference Snow Index (NDSI) from MODIS to estimate a composite NDSI which is then used for the determination of the SCA. The composite NDSI is derived using a machine learning-based regression. The experiments are performed for the high elevated regions of the Kunduz and Khanabad watershed of the northern Hindu Kush mountains for the peak winter and early melt season of 2019 corresponding to the months of February and March. The reference snow cover area for the evaluation of the results is generated by thresholding the NDSI derived from pan-sharpened Landsat-8 imagery.