cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17714885
title: Annex 1 - Detailed HRAA algorithm analysis.

description: Annex 1 - Detailed Human-Robot Activity Allocation (HRAA) algorithm analysis.<br> Supplemental material of the article: Human-Robot Activity Allocation Algorithm for the Redesign of Manual Assembly Systems into Human-Robot Collaborative Assembly;<br> Manuscript DOI: 10.1080/0951192X.2022.2083687;<br> Journal: International Journal of Computer Integrated Manufacturing.
DOI: 10.6084/m9.figshare.17714885.v1
title: Annex 1 - Detailed HRAA algorithm analysis.

description: Annex 1 - Detailed Human-Robot Activity Allocation (HRAA) algorithm analysis.<br> Supplemental material of the article: Human-Robot Activity Allocation Algorithm for the Redesign of Manual Assembly Systems into Human-Robot Collaborative Assembly;<br> Manuscript DOI: 10.1080/0951192X.2022.2083687;<br> Journal: International Journal of Computer Integrated Manufacturing.
DOI: 10.5281/zenodo.6595390
title: SuperDARN data in netCDF format (2007-Jan)

description: 2007-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6595389
title: SuperDARN data in netCDF format (2007-Jan)

description: 2007-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.6084/m9.figshare.12130980.v144
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.17632/t96hmx65pd.2
title: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 3 - dataset -Artificial Intelligence Models Applied to Biomedical Engineering  

description: “Artificial intelligence (AI) in Biomedical Engineering (BME)” is the use of smart device software to analyze complex data, classify, model, optimize, and predict many fields of medicine and healthcare. “AI Optimization in BME” refers to finding parameters for maximizing or minimizing objectives while satisfying the constraints of defined functions relative to some dataset of biomedical data. “AI Optimization in BME” results allow the comparison of different choices for determining which one might be the best. In the last decade the development in fields of medicine, healthcare, biomedicine, bioinformatics, etc.
Please read the chapter 3 at Science Direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000040
Section 3.3 Evolutionary algorithms for AI optimization in BME. 3.3.1 A typical evolutionary algorithm
Section 3.3.2 Genetic algorithms for AI optimization in BME. 3.3.2.1 Research 3.1 Genetic algorithm basic seven steps for selection by priorities. 
Section 3.3.3 Genetic algorithm for AI optimization in BME under MATLAB. 3.3.3.1 Research 3.2 Implementing genetic algorithm for AI optimization in BME with MATLAB.
3.3.4 General analysis and optimization of 2D and 3D data in biomedical engineering. 3.3.4 General analysis and optimization of 2D and 3D data in biomedical engineering. 3.3.6 MATLAB analysis and optimization of 3D data in biomedical engineering.
3.4 IBM Watson Studio for artificial intelligence. 3.4.1 IBM SPSS Modeler Flow
DOI: 10.17632/tpg8yvk9z3.1
title: Menstrual Bleeding, Chronic Pelvic Pain, and Quality of Life 2021

description: Data from this set was used to investigate how a population of young adults experience chronic pelvic pain and heavy menstrual bleeding and the impacts on quality of life. The mixed methods study was conducted at a university in the Midwest of the United States. Participants (n=167) were between the ages of 18-25 years old. A branched logic question survey was administered. Three qualitative focus groups (n=16) were conducted.  This data set is for the survey conducted.
DOI: 10.17632/m53xds7yvt.2
title: EXPERIMENTAL RECORD-Slug Attenuation

description: Two phase Flow Loop Experimental Data recordings obtained during Slug flow attenuation experiment as described in the included schematic.
the raw data, the summary and its corresponding graphs and flow regime map are included in the excel file.
The recorded data show that using a closed end pipe as a dampening volume for the slug flow the recorded data, show that dampening volumes are effective in reducing the slug severity.
DOI: 10.17632/4wgxsd2r4r.1
title: Experimental Data for Successive Tensile Loading Experiments Using a Modified Sample Configuration for Compressive Loading Apparatuses

description: This dataset includes high-speed images and mechanical time series from Split Hopkinson Pressure Bar Layered Tension experiments. High-speed images can be used to perform digital image correlation. File SmithGriffith2022_Images.zip contains high speed images and the Microsoft Excel file TimeSeries_SmithGriffith2022.xlsx contains axial and circumferential stress, strain, and strain rate time series for Split Hopkinson Pressure Bar Layered Tension experiments. See the manuscript “Evolution of pulverized rocks by tensile loading during successive earthquakes” with its accompanying supporting information for details on data processing and analysis.
DOI: 10.17632/sxv2bjpzhx.2
title: Genetic variation in HIF-2α attenuates ventilatory sensitivity and carotid body growth in chronic hypoxia in high-altitude deer mice

description: The gene encoding HIF-2α, Epas1, has experienced a history of natural selection in many high-altitude taxa, but the functional role of mutations in this gene are still poorly understood. We investigated the influence of the high-altitude variant of Epas1 in North American deer mice (Peromyscus maniculatus) on control of breathing and carotid body growth during chronic hypoxia. We created hybrids between high- and low-altitude populations of deer mice (F2 intercross breeding design) to disrupt linkages between genetic loci so physiological effects of Epas1 variants could be examined on an admixed genomic background. In general, chronic hypoxia (4 weeks at 12 kPa O2) enhanced ventilatory chemosensitivity (assessed as the acute ventilatory response to hypoxic-hypercapnia), increased total ventilation and arterial O2 saturation during progressive poikilocapnic hypoxia, and increased haematocrit and blood haemoglobin content across genotypes. However, effects of chronic hypoxia on ventilatory chemosensitivity were attenuated in mice that were homozygous for the high-altitude Epas1 allele (Epas1H/H). Carotid body growth and glomus cell hyperplasia, which was strongly induced in Epas1L/L mice in chronic hypoxia, was completely absent in Epas1H/H mice. In contrast, chronic hypoxia increased metabolism and attenuated body temperature depression in hypoxia in Epas1H/H mice, but not in Epas1L/L or Epas1H/L mice. Genotype had no effects on haematological traits. These findings confirm the important role of HIF-2α in modulating ventilatory sensitivity and carotid body growth in chronic hypoxia, and show that genetic variation in Epas1 is responsible for evolved changes in the control of breathing and metabolism in high-altitude deer mice.
DOI: 10.17632/r25st3yfkk.2
title: Characterization of immune microenvironment infiltration and m6A regulator-mediated RNA methylation modification patterns in osteoarthritis

description: data from GEO（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48556）
Contributor(s)	Ramos YF, Bos SD, Lakenberg N, Böhringer S, den Hollander WJ, Kloppenburg M, Slagboom E, Meulenbelt I
Citation(s)	
Ramos YF, Bos SD, Lakenberg N, Böhringer S et al. Genes expressed in blood link osteoarthritis with apoptotic pathways. Ann Rheum Dis 2014 Oct;73(10):1844-53. PMID: 23864235
DOI: 10.17632/kbbprxr4nw.1
title: Dataset - Adversarial AutoEncoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement

description: This dataset consists of movements drawn with a wireless remote controller in an immersive VR environment for 6 different movements called "Cube," "Cylinder", "Heart", "Infinity", "Sphere" and "Triangle". 
Data were collected from 10 participants. Each movement was collected 3 times for each participant for each session and 3 sessions were performed thus providing a total of 9 repetitions of each movement per participant.
The data were projected onto the frontal plane facing the head mounted display. The total number of movements collected in the database is 540.
Data were collected using Unity 2020.3.26f1 with the Oculus Rift S and resampled to 32 points.

A Python script is also included with an example of how to load the data. The Python script was tested with:
   # Python - 3.8.8
   # Panda - 1.3.1
   # Numpy - 1.21.1
   # Matplotlib - 3.4.2
DOI: 10.17632/fnzyw82kpf.1
title: Indoor Tanning and MPM Supplemental Materials

description: Indoor tanning is associated with an increased risk for multiple primary melanomas: A case-control study
DOI: 10.17632/r8wfnb9n44.1
title: Mendeley Data - Biological Methodology

description: This work describes the methodology used to study the leishmanicidal and anti-inflammatory effects of 5,6-dihydro-5,6-dihydroxy-2-phenylquinoline (DFQ1) and 4’-hydroxy-2-phenylquinoline (DFQ2) that were, respectively, products of the biotransformation of 2-phenylquinoline (FQ) by Aspergillus fumigatus and Aspergillus flavus.
DOI: 10.17632/x77ynx7bh9.1
title: Schools as Learning Organization

description: The data set consists of 2 sections: i ) Demographic profile of the respondents ii) thirty-five items measuring Holistic, Strategic, Vision, Empowerment, Information, Internality, Learning, and Synergy from the learning organization scale. Data consist of 950 responses from school teachers through self-reported questionnaires.
DOI: 10.17632/b9dws5j6hr.1
title: LC-MS/MS lipidomics data: Enhancing the Accuracy of Surgical Debridement Following Burns Trauma via Application of Rapid Evaporative Ionisation-Mass Spectrometry (REIMS) 

description: LC-MS/MS lipidomics data from the manuscript titled: Enhancing the Accuracy of Surgical Debridement Following Burns Trauma via Application of Rapid Evaporative Ionisation-Mass Spectrometry (REIMS) 

Publication Abstract:
Background
Surgical debridement is a necessary procedure for burn patients that require the removal of eschar. The extent of debridement is currently guided by clinical judgement, with excess debridement of healthy tissue potentially leading to excessive scar, or inadequate debridement increasing risk of infection. Thus, an objective real-time measure to facilitate accurate debridement could support clinical judgement and improve this surgical procedure. This study was designed to investigate the potential use of Rapid evaporative ionisation mass spectrometry (REIMS) as a tool to support data-driven objective tissue debridement.

Methods
Data were acquired using a multi-platform approach that consisted of both Rapid Evaporative Ionisation Mass Spectrometry (REIMS) performed on intact skin, and comprehensive liquid chromatography-mass spectrometry (LC-MS/MS) lipidomics performed on homogenised skin tissue extracts. Data were analysed using principal components analysis (PCA) and multivariate orthogonal projections to latent squares discriminant analysis (OPLS-DA) and logistic regression to determine the predictability of the models. 

Results
PCA and OPLS-DA models of the REIMS and LC-MS/MS lipidomics data reported separation of debrided and healthy tissue. Molecular fingerprints generated from REIMS analysis of healthy skin tissue revealed a high degree of heterogeneity, however, intra-individual variance was smaller than inter-individual variance. Both platforms indicated high levels of skin classification accuracy. In addition, OPLS-DA of the LC-MS/MS lipidomic data revealed significant differences in specific lipid classes between healthy control and debrided skin samples; including lower free fatty acids (FFA), monoacylglycerols (MAG), lysophosphatidylglycerol (LPG) and lysophosphatidylethanolamines (LPE) in debrided tissue and higher lactosylceramides (LCER) and cholesterol esters (CE) compared to healthy control tissue. 

Conclusions
Having established the heterogeneity in the biochemical composition of healthy skin using REIMS and LC-MS/MS, our data show that REIMS has the potential to distinguish between debrided and healthy skin tissue samples. This pilot study suggests that REIMS may be an effective tool to support accurate tissue debridement during burn surgery.
DOI: 10.17632/bvkns2xxn2.1
title: The Functional connectivity of Basal Forebrain is associated with Superior Memory Performance in older adults: a case-control study

description: The Structural and resting-state functional MRI
DOI: 10.17632/z9jsfg8gbs.2
title: Programming Procedure in Excel for Calculating Model Variables in a New SEIR Epidemic Model Based on the latent-infectious period chronological order

description: Our new SEIR epidemic model built from the l-i AIR model [1] has similar terms to the conventional SEIR epidemic model [2]. We have uploaded an instruction file for describing how to write a calculation program in Excel for calculating the model variables S, E, I, R and y. 
Calculation equations and results in example 1 and example 2 are the same. The difference is that we used absolute cell reference for parameters l and c in example 2. Thus, if parameters l and c are changed, we can just change the value of l in cell D2 and the value of c in cell E2 in example 2. However, in example 1, we have to change codes related to parameters l and c in all cells from B29-Bn, C29-Cn, D29-Dn and E29-En.

REFERENCES
[1]	Liu, X. A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan. Results Phys 20, 103712 (2021).
[2]	Liu, X. Analytical Solution of a New SEIR Model Based on Latent Period-Infectious Period Chronological Order. medRxiv, https://doi.org/10.1101/2021.12.14.21267812, 2021.2012.2014.21267812 (2021).
DOI: 10.17632/b49g97k46t.1
title: Dataset for Structural features of DNA polymerases β and λ in complex with benzo[a]pyrene-adducted DNA cause a difference in lesion tolerance

description: The dataset is related to the article "Structural features of DNA polymerases β and λ in complex with benzo[a]pyrene-adducted DNA cause a difference in lesion tolerance" in DNA Repair journal. 
MP4 files are MD movies of 100 ns MD trajectory for all complexes mentioned in the article.
All .py files are the UCSF Chimera 1.15 sessions. The file "Pol_l_Pol_b_bpc_dna_100ns.py" contains two superimposed DNA molecules from Pol λ and Pol β complexes with the cis-DG-BP-adducted DNA duplex. The file "Pol_l_Pol_b_bpt_dna_100ns.py" consists of two superimposed DNA molecules from Pol λ and Pol β complexes with the trans-DG-BP-adducted DNA duplex. The files "Pol_l_Pol_b_bpc-100ns.py" and "Pol_l_Pol_b_bpt-100ns.py" are an overlay of two complex structures (Pol λ and Pol β in complex with cis-DG-BP DNA and trans-DG-BP DNA, respectively).
DOI: 10.17632/j7sx7d786n.1
title: Road polishing: High resoluted textures dataset of three mosaics made of different aggregates at different polishing stages

description: This database is composed of topographies of three mosaics of three different aggregates at three different polishing stages. Each topography is two different formats: “sur” and “txt”. The “txt” format is composed of 15 parallel profiles separated by 0.5 mm. Each profile has a length of 76 mm. Each two points of the profile are separated by 0.01 mm. The three circular mosaics of 22.5 cm diameter are made from three different aggregates: Limestone, Basalt, and Granite. They have been prepared by placing the 7.2-10 mm size fraction of the aggregates in a single layer as closely as possible, with their flattest faces lying on the bottom of a mold and then filling the mould with a resin. The polishing process is made using the dedicated head of Wehner-Schulze machine. The polishing head contains three rubber cones mounted on a rotary disc. It is the loaded and rolling action of these three cones on the pavement surface that produce the effect of polishing. To accelerate the polishing, a mix of 5% quartz powder in 95% water is sprinkled during the rotations of the cones. Texture has been captured using a laser scanning profilometer before, and after 90,000 and then 180,000 cycles of polishing. The measuring area is located inside the polished ring of the pavement.
DOI: 10.5281/zenodo.5052675
title: Experimental data on plastered rubble stone masonry walls

description: This repository contains data from experimental tests on plastered rubble stone masonry walls conducted at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. <strong>Data visualization in RENKU</strong>: Click here Please cite the following related publications: <br> [1] Rezaie, A., Godio, M., Beyer, K. (2020). Experimental investigation of strength, stiffness and drift capacity of rubble stone masonry walls. Construction and Building Materials, 251, 118972. [2] Rezaie, A., Achanta, R., Godio, M., Beyer, K. (2020). Comparison of crack segmentation using digital image correlation measurements and deep learning. Construction and Building Materials, 261, 120474. [3] Rezaie, A., Godio, M., & Beyer, K. (2021). Investigating the cracking of plastered stone masonry walls under shear–compression loading. Construction and Building Materials, 306, 124831. <br> [4] Rezaie, A., Godio, M., Achanta, R., & Beyer, K. (2022). Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns. <em>Automation in Construction</em>, <em>140</em>, 104313.
DOI: 10.5281/zenodo.5052674
title: Experimental data on plastered rubble stone masonry walls

description: This repository contains data from experimental tests on plastered rubble stone masonry walls conducted at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. <strong>Data visualization in RENKU</strong>: Click here Please cite the following related publications: <br> [1] Rezaie, A., Godio, M., Beyer, K. (2020). Experimental investigation of strength, stiffness and drift capacity of rubble stone masonry walls. Construction and Building Materials, 251, 118972. [2] Rezaie, A., Achanta, R., Godio, M., Beyer, K. (2020). Comparison of crack segmentation using digital image correlation measurements and deep learning. Construction and Building Materials, 261, 120474. [3] Rezaie, A., Godio, M., & Beyer, K. (2021). Investigating the cracking of plastered stone masonry walls under shear–compression loading. Construction and Building Materials, 306, 124831. <br> [4] Rezaie, A., Godio, M., Achanta, R., & Beyer, K. (2022). Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns. <em>Automation in Construction</em>, <em>140</em>, 104313.
DOI: 10.5281/zenodo.6620750
title: Data for MWHS and 2C-ICE collocations

description: This is the code and training dataset used in the corresponding article.
DOI: 10.5281/zenodo.6620749
title: Data for MWHS and 2C-ICE collocations

description: This is the code and training dataset used in the corresponding article.
DOI: 10.5523/bris.2iha22zobeher2leh936xrktqx
title: Data from Liu et al. 2022. Saccorhytus is an early ecdysozoan and not the earliest deuterostome. Nature

description: This dataset contains 25 Synchrotron Radiation X-ray Tomographic Microscopy (SRXTM) datasets based on specimens of Saccorhytus coronarius from the early Cambrian Kuanchuanpu Formation (late Llandovery to early Wenlock, Silurian) of Zhejiang, South China.