cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5607141
title: Subject-specific dimensions for designing clothing patterns

description: The data set provided as a MATLAB (R2020a) binary file consists of 4 matrices. Each matrix has the size N times 23 and corresponds to the group of the Chinese adults (matrix adults_CN), the Czech adults (matrix adults_CZ), the Czech children (matrix children_CN) and the Czech adolescents (matrix teens_CZ). N is the number of measured subjects, where on each subjects, 23 anthropometric dimensions are measured. The dimensions in millimeters are consequently the total body height, the chest circumference (widest), the waist circumference (narrowest), the waist circumference (trousers), the hip circumference (widest), the shoulder width, the back width, the front length from neck to waist, the back length from neck to waist, the length from neck to shoulder, the sleeve length from neck to wrist, the arm circumference, the forearm circumference, the wrist circumference, the neck circumference, the length from crotch to knee, the length from crotch to ankle, the thigh circumference, the knee circumference, the calf circumference (widest), the ankle circumference, the length from waist to ground and the head circumference.
DOI: 10.5281/zenodo.5607140
title: Subject-specific dimensions for designing clothing patterns

description: The data set provided as a MATLAB (R2020a) binary file consists of 4 matrices. Each matrix has the size N times 23 and corresponds to the group of the Chinese adults (matrix adults_CN), the Czech adults (matrix adults_CZ), the Czech children (matrix children_CN) and the Czech adolescents (matrix teens_CZ). N is the number of measured subjects, where on each subjects, 23 anthropometric dimensions are measured. The dimensions in millimeters are consequently the total body height, the chest circumference (widest), the waist circumference (narrowest), the waist circumference (trousers), the hip circumference (widest), the shoulder width, the back width, the front length from neck to waist, the back length from neck to waist, the length from neck to shoulder, the sleeve length from neck to wrist, the arm circumference, the forearm circumference, the wrist circumference, the neck circumference, the length from crotch to knee, the length from crotch to ankle, the thigh circumference, the knee circumference, the calf circumference (widest), the ankle circumference, the length from waist to ground and the head circumference.
DOI: 10.6084/m9.figshare.16892680.v2
title: Rural Tourism post COVID La Palma

description: Data used in <i>Rural Tourism Networking during 2008 and Covid-19 crisis: A gender perspective</i>
DOI: 10.5517/ccdc.csd.cc1zn4rj
title: CCDC 1836618: Experimental Crystal Structure Determination

description: Related Article: Khalid Karrouchi, Saad Fettach, Mukesh M. Jotani, Abir Sagaama, Smaail Radi, Hazem A. Ghabbour, Yahia N. Mabkhot, Benacer Himmi, My El Abbes Faouzi, Noureddine Issaoui|2020|J.Mol.Struct.|1221|128800|doi:10.1016/j.molstruc.2020.128800
description: Related Article: Khalid Karrouchi, Saad Fettach, Ömer Tamer, Davut Avci, Adil Başoğlu, Yusuf Atalay, Zeynep Ayaz, Smaail Radi, Hazem A. Ghabbour, Yahia N. Mabkhot, My El Abbes Faouzi, M'hammed Ansar|2022|J.Mol.Struct.|1248|131506|doi:10.1016/j.molstruc.2021.131506
DOI: 10.6084/m9.figshare.16892659.v1
title: Rural Tourism PreCOVID La Palma

description: Data used in <i>Rural Tourism Networking during 2008 and Covid-19 crisis: A gender perspective</i>
DOI: 10.5519/qd.4xp1zqmd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.5607321
title: LMD Inconel 718 Random Tracks 12 versions 2021-03-03

description: Description of dataset 10.5281/zenodo.5607321 12 versions of deposition of 4 "random pattern" tracks<br> Inconel 718 tracks with process parameters:<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430<br> Power and velocity used in each experiment are described in the CNC file name (and g-code) The dataset is constituted by 12 .zip files (exp01.zip to exp12.zip), each one containing: - G-code part program, with name code Exp[n]_RandomTrack_4_rot_False_V[v]_P[p], where:<br> - [n] is the experiment index (1-12)<br> - [v] is the used deposition velocity (in mm/min)<br> - [p] is the used laser power (in W)<br> - trAll_ONEms.csv containing:<br> - t: timestamp in ms.<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power
DOI: 10.5281/zenodo.5607320
title: LMD Inconel 718 Random Tracks 12 versions 2021-03-03

description: Description of dataset 10.5281/zenodo.5607321 12 versions of deposition of 4 "random pattern" tracks<br> Inconel 718 tracks with process parameters:<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430<br> Power and velocity used in each experiment are described in the CNC file name (and g-code) The dataset is constituted by 12 .zip files (exp01.zip to exp12.zip), each one containing: - G-code part program, with name code Exp[n]_RandomTrack_4_rot_False_V[v]_P[p], where:<br> - [n] is the experiment index (1-12)<br> - [v] is the used deposition velocity (in mm/min)<br> - [p] is the used laser power (in W)<br> - trAll_ONEms.csv containing:<br> - t: timestamp in ms.<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power
DOI: 10.5281/zenodo.5607352
title: Effectiveness of a multicomponent intervention consisting of education and feedback on reducing benzodiazepine prescriptions by general practitioners: BENZORED hybrid type I cluster randomized controlled trial.

description: Complete dataset variables: GP_ID<br> Health_District<br> Health_District_name<br> PHC_ID<br> PHC_ID_name<br> Arm<br> DHD_Baseline<br> DHD_12m<br> PercentageBZD_baseline<br> PercentageBZD_12m<br> PercentageBZD_baseline_age65<br> PercentageBZD_12m_age65
DOI: 10.6084/m9.figshare.16786099.v1
title: Patients and Family Members Perspectives on The Use of Bedside Whiteboard and Its Implication to Treatment Outcomes and Patient Satisfaction: A Pilot Survey in General and Tertiary Hospitals in Makkah, Saudi Arabia

description: <sup>Effective communication between the members of the healthcare team and the patient and families are essential components for patient-centered care, it improves patient outcomes. Likewise, poor communication between healthcare team members and the patient and families is associated with poor patient outcomes. Due to several communication challenges, the use of visual tools such as bedside whiteboard to enhance communication has been widely implemented; however, the exact benefit of its use from patient perspective is still understudied. Thus, this study aims to determine the impact of bedside whiteboard use in facilitating patient’s involvement to achieving goal the day’s goal, adherence to pain medication schedule and use, tests and procedures schedule, patients and family engagement with the healthcare team, and discharge planning. This study is a descriptive cross-sectional study conducted among 335 patients and/or family members admitted in six hospitals in Makkah city: Al Noor hospital, Hera hospital, King Abdul-Aziz hospital, King Abdullah Medical City, King Faisal specialist hospital, and Maternity and Children hospital. The survey questionnaire used was adapted from previous studies and revalidated to ensure its suitability to the study subjects. Data collection was performed from August 1 to 31, 2020 and data analysis was done with SPSS software. Of the 335 participants, majority were satisfied to bedside white board for its use (4.36), and they recommend it to be used in hospitals because they perceived that through this medium of communication, their connection with the healthcare team will be easier (4.35). In regression analysis, the day’s goal and patient and/or family engagement are found to be the significant predictors of patient’s satisfaction level. Therefore, whiteboard is a useful tool to help patients achieve their daily goals and to improve their engagement with the healthcare team which also impacts on patient satisfaction. This study was approved by the Institutional Review Board of King Abdullah Medical City, Makkah, Saudi Arabia. All patients and relatives who agreed to participate in the online survey, completely filled-in and submit the survey questionnaire was accepted as implied consent. All information. Data entered into the Microsoft Excel®️ and SPSS was coded and rendered anonymous as to patient identity. All collected information relative to the patient's identity is considered confidential.</sup><br>
DOI: 10.11583/dtu.16895254
title: Eastern Baltic cod size at maturity and body condition

description: The dataset includes time series (1991-2021) of two biological indicators characterizing health of the Eastern Baltic cod (Gadus morhua) stock.<br>i) Size at maturity (L50, in cm) is the length at which half of the population have become mature. It is calculated for female and male fish combined. Further details on the data and L50 calculation can be found in the enclosed Working Document.ii) Cod body condition is represented by Fulton's K condition index, which is calculated as whole weight of the fish divided by its total length in the power of 3.<br>The calculation of both L50 and Fulton's K indices are based on data from 1st quarter Baltic International Bottom Trawl survey. Raw data for individual fish are available from International Council for Exploration of the Sea (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx)<br>
DOI: 10.25721/3s3n-qd96
title: Bourrée bourbonnaise
DOI: 10.25646/9183
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.16895824
title: Additional file 2-7

description: Additional file 2 · Table S1: TPM values of scRNA-Seq for patient-derived cell lines Additional file 3 · Table S2: Cell clustering results of Sharma et al dataset · Table S3: Cell clustering results of Purum et al dataset Additional file 4 · Table S4: ITTH score and drug response · Table S5: CIBERSORT's TCGA deconvolution · Table S6: CIBERSORT's signature matrix Additional file 5 · Table S7: GDSC drug statistics · Table S8: GDSC cell lines Additional file 6 · Table S9: Drug combinatorial prediction · Table S10: Monotherapy prediction Additional file 7 · Table S11: Drug dosages in experimental validation · Table S12: Experimental results of monotherapy · Table S13: Experimental results of drug combination
DOI: 10.5281/zenodo.5600493
title: Experimental observations about rupture nucleation process

description: Dataset of experiments carried out in order to observe the rupture nucleation phase with heterogeneous interface. The is the strain gage data for each experiment and the videogram of each event in .mat format file.
DOI: 10.17043/katrantsiotis-2021-sokli-1
title: Organic geochemical data including leaf wax hydrogen isotopes from Sokli, Northern Finland
DOI: 10.21942/uva.16884703
title: A high-resolution multi-shell 3T diffusion magnetic resonance imaging dataset as part of the Amsterdam Ultra-high field adult lifespan database (AHEAD) - dataset

description: In order to further our understanding of brain function and the underlying networks, more advanced diffusion weighted magnetic resonance imaging (DW MRI) data are essential. Here we present freely available high-resolution multi-shell multi-directional 3 Tesla (T) DWI MRI data as part of the ‘Amsterdam Ultra-high field adult lifespan database’ (AHEAD). The 3T DWI AHEAD dataset include 1.28mm isotropic whole brain DWI data of 49 healthy adult participants between 18 and 90 years old. The acquired data include DWIs at three non-zero b-values (48 directions, b-value 700 s/mm<sup>2</sup>; 56 directions, b-value 1000 s/mm<sup>2</sup>; 64 directions, b-value 1600s/mm<sup>2</sup>) including a total of twelve volumes with a b-value of 0 s/mm<sup>2</sup> (b0 volumes). In addition, eight b0 volumes with a reversed phase encoding direction were acquired to correct for distortions. To facilitate future use, the DWI data have been denoised, corrected for eddy currents, susceptibility-induced off-resonance field distortions, bias fields, and are skull stripped.<i> </i>
DOI: 10.6084/m9.figshare.16895908.v1
title: February 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in February, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895908
title: February 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in February, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.13099949
title: Supplementary Table 2: Reasons for exclusion

description: Some data were not collected or were lost due to factors including scheduling constraints/study overlap, ECG artifact, or acute mountain sickness (AMS) symptoms on day of testing.<br>a = AMS b = ECG artifact c = Scheduling constraints d= Did not participate <br>
DOI: 10.6084/m9.figshare.16895926.v1
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in September, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895926
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in September, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895941
title: February 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895941.v1
title: February 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895947
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895947.v1
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895953
title: August 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in August, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895953.v1
title: August 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in August, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5519/qd.p4f3ps5b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25721/209j-2y17
title: Messe de l'Epiphanie (programme musical). Saint le Seigneur Hosanna