cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.822537
title: The Annotated Corpus of Classical Tibetan (ACTib), Part II - POS-tagged version, based on the BDRC digitised text collection, tagged with the Memory-Based Tagger from TiMBL

description: This corpus is a part-of-speech tagged version of Wallman, Jeff, Rowinski, Zach, Ngawang Trinley, Tomlinson, Chris, & Keutzer, Kurt. (2017). Collection of Tibetan etexts compiled by the Buddhist Digital Resource Center [Data set]. Zenodo. http://doi.org/10.5281/zenodo.821218 using the training data of Hill, Nathan W., & Garrett, Edward. (2017). A part-of-speech (POS) tagged corpus of Classical Tibetan [Data set]. Zenodo. http://doi.org/10.5281/zenodo.574878 Please note that the files are not post-processed or manually corrected and that a small number of files in the KarmaDelek directory were still annotated, although the original xml-input was corrupted already. using the memory based tagger of https://languagemachines.github.io/mbt/
DOI: 10.5281/zenodo.822536
title: The Annotated Corpus of Classical Tibetan (ACTib), Part II - POS-tagged version, based on the BDRC digitised text collection, tagged with the Memory-Based Tagger from TiMBL

description: This corpus is a part-of-speech tagged version of Wallman, Jeff, Rowinski, Zach, Ngawang Trinley, Tomlinson, Chris, & Keutzer, Kurt. (2017). Collection of Tibetan etexts compiled by the Buddhist Digital Resource Center [Data set]. Zenodo. http://doi.org/10.5281/zenodo.821218 using the training data of Hill, Nathan W., & Garrett, Edward. (2017). A part-of-speech (POS) tagged corpus of Classical Tibetan [Data set]. Zenodo. http://doi.org/10.5281/zenodo.574878 Please note that the files are not post-processed or manually corrected and that a small number of files in the KarmaDelek directory were still annotated, although the original xml-input was corrupted already. using the memory based tagger of https://languagemachines.github.io/mbt/
DOI: 10.6084/m9.figshare.14511609
title: data and script for DDA/MIC antibiotic resistance in commensal Neisseria

description: Data and scripts for Goytia et al. (2021) MDPI Antitbiotics
DOI: 10.6084/m9.figshare.14511609.v1
title: data and script for DDA/MIC antibiotic resistance in commensal Neisseria

description: Data and scripts for Goytia et al. (2021) MDPI Antitbiotics
DOI: 10.6084/m9.figshare.12896309
title: An updated dataset of the global occurrence of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 (Brachyura: Portunidae)

description: The Atlantic blue crab Callinectes sapidus is a portunid native to the western Atlantic, from New England to Uruguay. The species was introduced in Europe in 1901 and in recent years it has become invasive; additionally, a significant northward expansion has been emphasized also in its native range. Here we present a harmonized global compilation of occurrences of the Atlantic blue crab Callinectes sapidus from native and non-native distribution ranges. Data are derived from online databases (GBIF, BISON, OBIS, and iNaturalist) as well as from peer-reviewed literature and unpublished sources gathered from personal communications and from available-on-request databases either directly maintained by the Hellenic Centre for Marine Research (HCMR; https://www.hcmr.gr), or connected with the HCMR, such as the Ellenic Network on Aquatic Invasive Species (ELNAIS; https://elnais.hcmr.gr). The dataset consists of 40,388 geo-referenced occurrences, 39,824 from native and 564 from non-native ranges, recorded in 53 countries. It contains taxonomic information (i.e., scientific name together with the name of who described the taxon originally complemented by the unique identifier of the species provided by the World Register of Marine Species - WoRMS; www.marinespecies.org), the sources from which the records were collated with the original identification codes if available, spatial and temporal information (i.e., coordinates in decimal degrees and year of record; if available also the month and day of record were included), and the country where the occurrences were recorded. In addition, flagging fields were included to categorise i) the status of the species at the record’s location (i.e., native or non-native) supplemented by notes identifying uncertain taxonomic identifications and records associated with unsuccessful introductions; ii) the most recent records with different coordinates to the third decimal, complemented by an additional field flagging each entry with unique coordinates with a progressively increasing integer, while less recent records in the same location are flagged by the same integer and iii) records located under fully marine conditions, in-land records, and those located in coastal areas. The dataset is complemented by a complete list of literature sources, including permanent identifiers (bibliographic Citation DOI) if available, a table describing the details of each flagging field, and a list of notes related with outliers and unsuccessful intentional introductions outside the native range. Notes are numbered according to the flags included in the dataset.<br>
DOI: 10.6084/m9.figshare.14511555
title: data for sheep model.xls

description: Data for logistic regression model for factors associated with farmers antibiotic attitude group.
DOI: 10.5281/zenodo.4727857
title: SHSY5Y_differentiation_datafiles

description: SHSY5Y_differentiation_datafiles is a directory of data accompanying the manuscript: <strong>Long read sequencing reveals novel isoforms and insights into splicing regulation during cell state changes</strong> David J Wright<sup>1</sup>, Nicola Hall<sup>2,3</sup>, Naomi Irish<sup>1</sup>, Angela L Man<sup>1</sup>, Will Glynn<sup>1</sup>, Arne Mould<sup>2,3</sup>, Alejandro De Los Angeles<sup>2,3</sup>, Emily Angiolini<sup>1</sup>, David Swarbreck<sup>1</sup>, Karim Gharbi<sup>1</sup>, Elizabeth M Tunbridge<sup>2,3</sup>, Wilfried Haerty<sup>1</sup>* <sup>1</sup>Earlham Institute, Norwich Research Park, Norfolk NR4 7UZ, UK <sup>2</sup>Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxfordshire OX3 3JX, UK <sup>3</sup>Oxford Health NHS Foundation Trust, Oxford, Oxfordshire OX3 7JX, UK --- It consists of subdirectories of differential expression (DE), differential transcript usage (DTU), synthetic spike-in assessments (SEQUINS), a gene specific case study (CACNA2D2) and a custom annotation GTF and corresponding transcriptome fasta (ANNOTATION), along with a README.txt describing contents. These data are accompanied by the corresponding analysis scripts that can be accessed from: <strong>https://github.com/TGAC/SHSY5Y_differentiation. </strong>
DOI: 10.34740/kaggle/dsv/2173816
title: Nepal ICU and Ventilators Occupancy for COVID
DOI: 10.5517/ccdc.csd.cc27n3qr
title: CCDC 2074914: Experimental Crystal Structure Determination

description: Related Article: A. Linden, T.S. Basu Baul|2021|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|77|209|doi:10.1107/S2053229621003545
DOI: 10.25345/c5cg0r
title: MassIVE MSV000087337 - Endophyte_project_part2_molecular_networking
title: second part of test after remeasuring samples with centeroid mode
DOI: 10.5281/zenodo.4727774
title: lulcc-bph-clouds data repository

description: This data repository containing the final results of the following study: G. Duveiller, F. Filiponni, A. Ceglar, J. Bojanowski, R. Alkama & A. Cescatti. Widespread cloud enhancement adds further value to the world's forests. <em>Nature Communications</em> (in press). The data repository is associated with a separate and dedicated code repository (DOI: 10.5281/zenodo.4727822), and it contains two types of data: folder <code>study_results</code> contains the final data generated in the study. The code necessary to produce this data can be found under the <code>code/process</code> folder in the code repository folder <code>data_4_figures</code> contains a more condensed format of the data, providing what is required to reproduce the figures in the paper. The code necessary to <em>harvest</em> this data from the larger data results stored in the folder <code>study_results</code> can be found in the <code>code/harvest</code> folder of the code repository. Finally, the code to generate the figures from the folder <code>data_4_figures</code> can be found in the <code>code/figures</code> folder of the code repository.
DOI: 10.5281/zenodo.4727773
title: lulcc-bph-clouds data repository

description: This data repository containing the final results of the following study: G. Duveiller, F. Filiponni, A. Ceglar, J. Bojanowski, R. Alkama & A. Cescatti. Widespread cloud enhancement adds further value to the world's forests. <em>Nature Communications</em> (in press). The data repository is associated with a separate and dedicated code repository (DOI: 10.5281/zenodo.4727822), and it contains two types of data: folder <code>study_results</code> contains the final data generated in the study. The code necessary to produce this data can be found under the <code>code/process</code> folder in the code repository folder <code>data_4_figures</code> contains a more condensed format of the data, providing what is required to reproduce the figures in the paper. The code necessary to <em>harvest</em> this data from the larger data results stored in the folder <code>study_results</code> can be found in the <code>code/harvest</code> folder of the code repository. Finally, the code to generate the figures from the folder <code>data_4_figures</code> can be found in the <code>code/figures</code> folder of the code repository.
DOI: 10.25500/edata.bham.00000654
title: Research data supporting the thesis "Systems level analysis of non-model organisms: a tool for understanding environmental stress"
DOI: 10.6084/m9.figshare.11876226
title: Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model

description: Tourism trend prediction is useful for tourism investment and tourism income estimation. Studies on tourism prediction have mostly relied on linear models and historical visitors; however, relationships between tourism trends and their influencing factors may be nonlinear. This study took internet search data as influencing factors and predicted tourism trends using a spatial-temporal framework based on the extreme gradient boosting (XGBoost) method. To incorporate the spatial characteristics, Baidu index data were divided according to locational attributes, and influencing factors were reconstructed via spatial cluster analysis and principal component analysis. Next, variables derived from dimension reduction were further processed based on the weighted moving average to reduce the lag effect between tourism internet search and actual tourism behavior. By using the above spatial-temporal method, Baidu index data can more accurately reflect changes in tourist source composition and tourist volumes. The spatial-temporal XGBoost composite model then applied to the empirical prediction of Beijing's tourism trends. A comparison of prediction results obtained using different models indicates that the spatial-temporal XGBoost composite model has excellent prediction ability. The findings also suggest that machine learning methods may not perform well if the essential characteristics of data, such as spatial autocorrelation and spatial heterogeneity, are ignored.
DOI: 10.6084/m9.figshare.11876226.v2
title: Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model

description: Tourism trend prediction is useful for tourism investment and tourism income estimation. Studies on tourism prediction have mostly relied on linear models and historical visitors; however, relationships between tourism trends and their influencing factors may be nonlinear. This study took internet search data as influencing factors and predicted tourism trends using a spatial-temporal framework based on the extreme gradient boosting (XGBoost) method. To incorporate the spatial characteristics, Baidu index data were divided according to locational attributes, and influencing factors were reconstructed via spatial cluster analysis and principal component analysis. Next, variables derived from dimension reduction were further processed based on the weighted moving average to reduce the lag effect between tourism internet search and actual tourism behavior. By using the above spatial-temporal method, Baidu index data can more accurately reflect changes in tourist source composition and tourist volumes. The spatial-temporal XGBoost composite model then applied to the empirical prediction of Beijing's tourism trends. A comparison of prediction results obtained using different models indicates that the spatial-temporal XGBoost composite model has excellent prediction ability. The findings also suggest that machine learning methods may not perform well if the essential characteristics of data, such as spatial autocorrelation and spatial heterogeneity, are ignored.
DOI: 10.6084/m9.figshare.11876226.v1
title: Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model

description: Tourism trend prediction is useful for tourism investment and tourism income estimation. Studies on tourism prediction have mostly relied on linear models and historical visitors; however, relationships between tourism trends and their influencing factors may be nonlinear. This study took internet search data as influencing factors and predicted tourism trends using a spatial-temporal framework based on the extreme gradient boosting (XGBoost) method. To incorporate the spatial characteristics, Baidu index data were divided according to locational attributes, and influencing factors were reconstructed via spatial cluster analysis and principal component analysis. Next, variables derived from dimension reduction were further processed based on the weighted moving average to reduce the lag effect between tourism internet search and actual tourism behavior. By using the above spatial-temporal method, Baidu index data can more accurately reflect changes in tourist source composition and tourist volumes. The spatial-temporal XGBoost composite model then applied to the empirical prediction of Beijing's tourism trends. A comparison of prediction results obtained using different models indicates that the spatial-temporal XGBoost composite model has excellent prediction ability. The findings also suggest that machine learning methods may not perform well if the essential characteristics of data, such as spatial autocorrelation and spatial heterogeneity, are ignored.
DOI: 10.17632/6mb8jhcwz2.1
title: OTS SAMs

description: AFM image of compact OTS SAM on bare silicon substrate showing the OTS film thickness and height histograms of smooth OTS film surfaces used to determine the OTS RMS roughness
DOI: 10.34740/kaggle/dsv/2173743
title: Hospital Ambient Noise Dataset
DOI: 10.17632/sg3d7dv345.1
title: Neckel et al CatWalk and RAGT data

description: Our group strives to restore overground locomotion following spinal cord injuries through the use of robot-assisted gait training (RAGT) . Specifically, following a C4/5 over-hemisection injury, we train young adult female rats in the Robomedica Rodent Robotic Motor Performance System (RRMPS) for 4 weeks. We assess their performance within the device, as well as any changes in unassisted overground locomotion with the CatWalk gait analysis device. The MATLAB data provided here contains the raw RRMPS output, and select CatWalk variables (x/y position, initial contact/ toe-off time) from all rats, from all groups, from all studies. For more details, please see the following papers; Neckel ND, Dai HN, Burns MP. A novel multidimensional analysis of rodent gait reveals the compensation strategies employed during spontaneous recovery from spinal cord and traumatic brain injury. J of neurotrauma. 2018 Oct 20(ja). Neckel ND. Novel spatiotemporal analysis of gait changes in body weight supported treadmill trained rats following cervical spinal cord injury. J Neuroeng Rehab. 2017; 14(1):96. Neckel ND. Methods to quantify the velocity dependence of common gait measurements from automated rodent gait analysis devices. J Neurosci Methods. 2015 Sep 30;253:244-53. Neckel ND, Dai H, Bregman BS. Quantifying changes following spinal cord injury with velocity dependent locomotor measures. J Neurosci Methods. 2013 Mar 30;214(1):27-36.
DOI: 10.18746/bmth.data.00000157
title: Footprint data
DOI: 10.6084/m9.figshare.14511657
title: Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model

description: Tourism trend prediction is useful for tourism investment and tourism income estimation. Studies on tourism prediction have mostly relied on linear models and historical visitors; however, relationships between tourism trends and their influencing factors may be nonlinear. This study took internet search data as influencing factors and predicted tourism trends using a spatial-temporal framework based on the extreme gradient boosting (XGBoost) method. To incorporate the spatial characteristics, Baidu index data were divided according to locational attributes, and influencing factors were reconstructed via spatial cluster analysis and principal component analysis. Next, variables derived from dimension reduction were further processed based on the weighted moving average to reduce the lag effect between tourism internet search and actual tourism behavior. By using the above spatial-temporal method, Baidu index data can more accurately reflect changes in tourist source composition and tourist volumes. The spatial-temporal XGBoost composite model then applied to the empirical prediction of Beijing's tourism trends. A comparison of prediction results obtained using different models indicates that the spatial-temporal XGBoost composite model has excellent prediction ability. The findings also suggest that machine learning methods may not perform well if the essential characteristics of data, such as spatial autocorrelation and spatial heterogeneity, are ignored.
DOI: 10.6084/m9.figshare.14511075.v1
title: DDA_cNpNRef_imageJ_alldata_R.csv

description: Contains Data for analysis, Goytia et al. MDPI Antibiotics, 2021, Fig 1
DOI: 10.6084/m9.figshare.14511075
title: DDA_cNpNRef_imageJ_alldata_R.csv

description: Contains Data for analysis, Goytia et al. MDPI Antibiotics, 2021, Fig 1
DOI: 10.6084/m9.figshare.14511072
title: MIC_cNpNRef_all_R.csv

description: Data table for Minimal Inhibitory concentration analysis, Fig 3, Goytia et al (2021) MDPI Antibiotics
DOI: 10.5281/zenodo.4727909
title: Britain Breathing 2020 Air Quality and Meteorological Dataset

description: This data set is a collection of daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK for the year 2020. The dataset contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model. The tools used to download and process these measurement datasets are available here: https://zenodo.org/record/4545257 The dataset is designed for use with the region estimator toolset, available here: https://zenodo.org/record/4589993 Emissions over the UK for the EMEP model runs were generated using the NAEI 2016 UK emission dataset, available in netcdf form here: https://zenodo.org/record/3997165#.X9KUBF6nzUI. The running scripts, and operation inputs for EMEP, are available here: https://zenodo.org/record/3997301#.X9KUAF6nzUI and https://zenodo.org/record/3997271#.X9KV1F6nzUI. This dataset is an extension of the Britain Breathing 2016-2019 Air Quality and Meteorological Dataset, available here: https://zenodo.org/record/4416028#.YIrJfOvTXUL. The file 'site_location_data.csv' in this dataset contains the location metadata (site_id, latitude, longitude, postcode area) for the dataset here too. The dataset is presented in CSV format as the file 'turing_aq_daily_met_pollen_pollution_data_2020.csv', described below. The columns intended to be used as indexes are: timestamp, date of measurements on that row site_id, measurement site ID, corresponding to sites in the three networks: AURN [indicated by AQ], MIDAS [indicated by WEATHER], or pollen [indicated by POLLEN]. The data columns are: O3, PM10, PM2.5, NO2, NOXasNO2, SO2, daily mean and maximum values in ug/m3 (all with "_max", "_mean", and "_flag" tags) AURN measurement data O3_EMEP, NO2_EMEP, SO2_EMEP, NOXasNO2_EMEP, PM2.5_EMEP, PM10_EMEP, daily mean and maximum values in ug/m3 (all with "_max", and "_mean" tags) EMEP model forecasts alnus, ambrosia, artemisia, betula, corylus, fraxinus, platanus, poaceae, quercus, salix, ulmus, urtica, daily pollen grain counts temperature, relativehumidity, pressure, daily mean and maximum values in degC, %, and hPa (all with "_max", "_mean", and "_flag" tags). Met Office measurement data The "_flag" columns indicate data points which have been partially, or fully, imputed. The values for these will be in the range 0-1, and indicate the fraction of the hourly values within that day that are imputed (0 = none, 1 = all 24 hourly datapoints are imputed). No imputation has been carried out for this dataset, so the "_flag" data in this dataset will always be zero (the number of hourly data points used to calculate the daily mean and maximum are not recorded in this dataset).
DOI: 10.6084/m9.figshare.12251468.v3
title: The Diorisis Ancient Greek Corpus (JSON)

description: An updated JSON version of the Diorisis Ancient Greek Corpus optimised for use with the Diorisis Search desktop application (https://www.crs.rm.it/diorisissearch/).Current version: 1.5
DOI: 10.21227/0g0a-fe06
title: Handball action dataset - UNIRI-HBD

description: The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions. The scenes were shot with stationary GoPro cameras that were mounted on the left or right side of the playground, from different angles. The videos were recorded in at least full HD (1920 × 1080) resolution at 30 or more frames per second. On average 12 players appear in each scene, but mostly one or two players perform the action of interest. The performances of the actions of interest are extracted in separate video clips showing only that action in isolation. The isolated action clips are obtained first by automatic detection of players using Mask RCNN detector and tracking with Deep SORT, then by manual selection of appropriate tracked player.
DOI: 10.11583/dtu.14501598.v1
title: Precipitation data from a disdrometer installed on the top of the Risø met mast, Denmark

description: This dataset consists of 1-minute and 20-seconds precipitation measurements observed with a OTT Parsivel2 installed on the top of the tall meteorological mast at DTU Risø campus. The period includes more than 2 years of measurements.
DOI: 10.11583/dtu.14501598
title: Precipitation data from a disdrometer installed on the top of the Risø met mast, Denmark

description: This dataset consists of 1-minute and 20-seconds precipitation measurements observed with a OTT Parsivel2 installed on the top of the tall meteorological mast at DTU Risø campus. The period includes more than 2 years of measurements.
DOI: 10.11583/dtu.14501601
title: Quality controlled precipitation data from a disdrometer installed at on the top of the Risø met mast, Denmark

description: This dataset consists of 1-minute and 20-seconds precipitation measurements observed with a OTT Parsivel2 installed on the top of the tall meteorological mast at DTU Risø campus. The period includes more than 2 years of measurements.