cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7079711
title: Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid

description: Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from our Osborne Mine dataset. <strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please <strong>cite the original authors</strong> when using this dataset. <strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4. <strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419 <strong>Source license: </strong>CC-BY <strong>Repository: </strong>https://github.com/fatiando-data/lightning-creek-magnetic-grid
DOI: 10.5281/zenodo.7079710
title: Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid

description: Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from our Osborne Mine dataset. <strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please <strong>cite the original authors</strong> when using this dataset. <strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4. <strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419 <strong>Source license: </strong>CC-BY <strong>Repository: </strong>https://github.com/fatiando-data/lightning-creek-magnetic-grid
DOI: 10.5281/zenodo.7051692
title: Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2

description: This archive contains part 2 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is contains data collected from several different sources and distributed under a CC BY NC SA 4.0 license. Part 1 of the data is available here. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai .
description: This work is supported by the Hasler Foundation, Cambridge University Press and Cambridge Assessment and DeepSea
description: {"references": ["Malinin, Andrey et al. (2022). \u00a0Shifts 2.0: Extending The Dataset of Real Distributional Shifts, arXiv:2206.15407"]}
DOI: 10.5281/zenodo.7051691
title: Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2

description: This archive contains part 2 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is contains data collected from several different sources and distributed under a CC BY NC SA 4.0 license. Part 1 of the data is available here. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai .
description: This work is supported by the Hasler Foundation, Cambridge University Press and Cambridge Assessment and DeepSea
description: {"references": ["Malinin, Andrey et al. (2022). \u00a0Shifts 2.0: Extending The Dataset of Real Distributional Shifts, arXiv:2206.15407"]}
DOI: 10.6084/m9.figshare.21118729
title: Non-synonymous heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering non-synonymous variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.6084/m9.figshare.21118729.v1
title: Non-synonymous heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering non-synonymous variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.5281/zenodo.7065053
title: Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"

description: Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing. The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab. This work is available in medRxiv and has been submitted
DOI: 10.5281/zenodo.7065052
title: Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"

description: Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing. The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab. This work is available in medRxiv and has been submitted
DOI: 10.5281/zenodo.7081409
title: After the Crowds Disperse

description: Over the past decade, cultural heritage organisations have increasingly turned to volunteers through citizen research (or crowdsourcing) platforms to help make their holdings digitally accessible for discovery and research. These platforms have proved to be highly successful, both in attracting and retaining the interest of volunteers, and in the rate and quality of data collected. They have provided a new and deeper level of engagement and attracted more diverse audiences to national collections. However, there are still significant hurdles to overcome to achieve seamless sharing and movement of data between institutional Collection Management Systems (CMS) and crowdsourcing platforms, and back again. If crowdsourcing is to fulfil the potential of its generous volunteers this break in the data cycle must be closed.
description: Attachment to Engaging Crowds Report
DOI: 10.5281/zenodo.7081408
title: After the Crowds Disperse

description: Over the past decade, cultural heritage organisations have increasingly turned to volunteers through citizen research (or crowdsourcing) platforms to help make their holdings digitally accessible for discovery and research. These platforms have proved to be highly successful, both in attracting and retaining the interest of volunteers, and in the rate and quality of data collected. They have provided a new and deeper level of engagement and attracted more diverse audiences to national collections. However, there are still significant hurdles to overcome to achieve seamless sharing and movement of data between institutional Collection Management Systems (CMS) and crowdsourcing platforms, and back again. If crowdsourcing is to fulfil the potential of its generous volunteers this break in the data cycle must be closed.
description: Attachment to Engaging Crowds Report
DOI: 10.17028/rd.lboro.21118741
title: Supplementary information files for Alterations in shoulder kinematics are associated with shoulder pain during wheelchair propulsion sprints

description: Supplementary information files for article Alterations in shoulder kinematics are associated with shoulder pain during wheelchair propulsion sprints <br> The study purpose was to examine the biomechanical characteristics of sports wheelchair propulsion and determine biomechanical associations with shoulder pain in wheelchair athletes. Twenty wheelchair court-sport athletes (age: 32±11 years old) performed one submaximal propulsion trial in their sports-specific wheelchair at 1.67m.s-1 for three minutes and two 10-second sprints on a dual-roller ergometer. The Performance Corrected Wheelchair User’s Shoulder Pain Index (PC-WUSPI) assessed shoulder pain. During the acceleration phase of wheelchair sprinting, participants propelled with significantly longer push times, larger forces and thorax flexion range of motion (ROM) than both the maximal velocity phase of sprinting and submaximal propulsion. Participants displayed significantly greater peak glenohumeral abduction and scapular internal rotation during the acceleration phase (20 ± 9° and 45 ± 7°) and maximal velocity phase (14 ± 4° and 44 ± 7°) of sprinting, compared to submaximal propulsion (12 ± 6° and 39 ± 8°). Greater shoulder pain severity was associated with larger glenohumeral abduction ROM (r = 0.59, P = 0.007) and scapular internal rotation ROM (r = 0.53, P = 0.017) during the acceleration phase of wheelchair sprinting, but with lower peak glenohumeral flexion (r = -0.49, P = 0.030), peak abduction (r = -0.48, P =0.034) and abduction ROM (r = -0.44, P = 0.049) during the maximal velocity phase. Biomechanical characteristics of wheelchair sprinting suggest this activity imposes greater mechanical stress than submaximal propulsion. Kinematic associations with shoulder pain during acceleration are in shoulder orientations linked to a reduced subacromial space, potentially increasing tissue stress. <br> <br>
DOI: 10.17028/rd.lboro.21118741.v1
title: Supplementary information files for Alterations in shoulder kinematics are associated with shoulder pain during wheelchair propulsion sprints

description: Supplementary information files for article Alterations in shoulder kinematics are associated with shoulder pain during wheelchair propulsion sprints <br> The study purpose was to examine the biomechanical characteristics of sports wheelchair propulsion and determine biomechanical associations with shoulder pain in wheelchair athletes. Twenty wheelchair court-sport athletes (age: 32±11 years old) performed one submaximal propulsion trial in their sports-specific wheelchair at 1.67m.s-1 for three minutes and two 10-second sprints on a dual-roller ergometer. The Performance Corrected Wheelchair User’s Shoulder Pain Index (PC-WUSPI) assessed shoulder pain. During the acceleration phase of wheelchair sprinting, participants propelled with significantly longer push times, larger forces and thorax flexion range of motion (ROM) than both the maximal velocity phase of sprinting and submaximal propulsion. Participants displayed significantly greater peak glenohumeral abduction and scapular internal rotation during the acceleration phase (20 ± 9° and 45 ± 7°) and maximal velocity phase (14 ± 4° and 44 ± 7°) of sprinting, compared to submaximal propulsion (12 ± 6° and 39 ± 8°). Greater shoulder pain severity was associated with larger glenohumeral abduction ROM (r = 0.59, P = 0.007) and scapular internal rotation ROM (r = 0.53, P = 0.017) during the acceleration phase of wheelchair sprinting, but with lower peak glenohumeral flexion (r = -0.49, P = 0.030), peak abduction (r = -0.48, P =0.034) and abduction ROM (r = -0.44, P = 0.049) during the maximal velocity phase. Biomechanical characteristics of wheelchair sprinting suggest this activity imposes greater mechanical stress than submaximal propulsion. Kinematic associations with shoulder pain during acceleration are in shoulder orientations linked to a reduced subacromial space, potentially increasing tissue stress. <br> <br>
DOI: 10.6084/m9.figshare.21118690.v1
title: Gene disruptive variants heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.6084/m9.figshare.21118780
title: Data of “Iron Variability Reveals the Interface Effects of Aerosol-Pollutant Interactions on the Glacier Surface of Qinghai-Tibet Plateau”

description: <strong>Data of “Iron Variability Reveals the Interface Effects of Aerosol-Pollutant Interactions on the Glacier Surface of Qinghai-Tibet Plateau”</strong>
DOI: 10.6084/m9.figshare.21118780.v1
title: Data of “Iron Variability Reveals the Interface Effects of Aerosol-Pollutant Interactions on the Glacier Surface of Qinghai-Tibet Plateau”

description: <strong>Data of “Iron Variability Reveals the Interface Effects of Aerosol-Pollutant Interactions on the Glacier Surface of Qinghai-Tibet Plateau”</strong>
DOI: 10.5281/zenodo.7065633
title: TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 4, January 2021 - August 2022)

description: <strong>TweetsCOV19</strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*. We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.7065632
title: TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 4, January 2021 - August 2022)

description: <strong>TweetsCOV19</strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*. We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices: Tweet Id: Long. Username: String. Encrypted for privacy issues*. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ). #Followers: Integer. #Friends: Integer. #Retweets: Integer. #Favorites: Integer. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;". Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1"). Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;". Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;". URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;" To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.7065612
title: TweetsKB (Part 10, January 2021 - December 2021)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.7065611
title: TweetsKB (Part 10, January 2021 - December 2021)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.6084/m9.figshare.21118711
title: Uncertainty and emergent constraints on enhanced ecosystem C stock by land greening

description: This fils includes the metadata used to reproduce results in the paper "Uncertainty and emergent constraints on enhanced ecosystem carbon stock by land greening "
DOI: 10.5281/zenodo.7065625
title: TweetsKB (Part 11, January 2022 - August 2022)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.5281/zenodo.7065624
title: TweetsKB (Part 11, January 2022 - August 2022)

description: <strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>2 billion tweets</strong>, spanning more than 7 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: https://data.gesis.org/tweetskb/. <em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em>
DOI: 10.6084/m9.figshare.21118690
title: Gene disruptive variants heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.6084/m9.figshare.21118690.v3
title: Gene disruptive variants heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.6084/m9.figshare.21118690.v2
title: Gene disruptive variants heatmap plots

description: The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.
DOI: 10.25540/51mk-kh4c
title: Understanding networks in rural Cambodian farming communities and how they influence antibiotic use

description: We conducted a study in two rural Cambodian farming communities (Takeo province) to investigate how social and contextual influences affect both human and animal antibiotic use behaviours. Data were collected in three phases: a baseline household census to enumerate village residents, a social network survey to understand village-level social ties, and in-depth interviews to elicit information about the influence of social ties on their decision-making processes. This dataset contains anonymised information about participants' social ties within their villages.