cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4d9zptw33g.1
title: Apollo Smart Walker Dataset

description: APOLLO SMART WALKER DATASET

Gait monitoring of elderly people who use walkers.
This dataset collects data from the accelerometer, gyroscope and magnetometer sensors of the smartphone positioned in the center of a walker.

ADL 1
Forward: Starting the walk on the sidewalk, passing through the grass to the end point.
Backward: Starting the walk on the grass, going to the sidewalk.

ADL 2
Forward: Starting the walk from an internal environment of the house and going to the external environment.
Backward: Starting the walk from an external environment of the house and going to the internal environment.

ADL 3 
Forward: Starting the walk in the living room, going through the hallway and entering the bathroom.
Backward: Starting the walk in the bathroom, going through the hallway and entering the living room.

See the records of this dataset:
ADL 1 https://www.youtube.com/playlist?list=PLRHcq8YHj4-aHt03LLV-tvM9IP90869dU
ADL 2 https://www.youtube.com/playlist?list=PLRHcq8YHj4-YaOt1KNg5KhCxcO1wuF_F_
ADL 3 https://www.youtube.com/playlist?list=PLRHcq8YHj4-YEc2PifRcFp0Aw1AJFIuXC

Number of samples: 180
Number of Instance: 482031
Data Set Characteristics:  Sequential Data, Time Series Data, Multivariate Data, Correlation
Area: Elderly Health, Applied Computing
Attribute Characteristics: Integer, Double
Number of Attributes: 5

The coordinate-system is defined relative to the screen of the phone in its default orientation. The axes are not swapped when the device's screen orientation changes.
The X axis is horizontal and points to the right, the Y axis is vertical and points up and the Z axis points towards the outside of the front face of the screen. In this system, coordinates behind the screen have negative Z values.
Source: https://developer.android.com/reference/android/hardware/SensorEvent

Apollo Smart Walker Collector github repository:
https://github.com/hartmannjean/Apollo-Smart-Walker-Collector

Relevant Information:
1. The dataset collects data from an Android smartphone which is positioned in the center of the walker;
2. The data sensors collected are accelerometer, gyroscope and magnetometer;
3. The sampling frequency of the sensors is DELAY_FASTEST - 20 milliseconds;
4. Data format: CSV.

Dataset information:
1. Dataset was recorded in video;
2. The dataset was recorded in three different scenarios. Each scenario has two turns, going forward and coming back.
3. Each scenario was recorded 20 times.
4. Each recording generated three files:
i. Accelerometer;
ii. Gyroscope;
iii. And magnetometer;
5. Each file contains the following information;
i. Timestamp, Id, x axis, y axis, z axis.

This dataset is part of this article paper:
Hartmann, Jean; Tavares, João (2022). Apollo Smart Walker: A Model Based on IoT and Artificial Intelligence for Monitoring the Gait of Elderly People Using Walkers.
Universidade do Vale do Rio dos Sinos.
DOI: 10.17632/rydj2r7vxk.1
title: Servicios Bibliotecarios durante la pandemia del COVID-19: Roles y desafíos en Chile.

description: Los resultados de la encuesta referente a los Servicios Bibliotecarios durante la pandemia del COVID-19: Roles y desafíos en Chile suponen un importante mapeo de las opiniones de los usuarios respecto al tema en cuestión, con preguntas como: ¿Cuál o Cuáles son los factores que limitan el óptimo uso de la tecnología en las bibliotecas durante la cuarentena? En el contexto pandémico: ¿Qué acciones se podrían implementar en las bibliotecas (académicas o públicas) nacionales para mejorar los servicios de acceso a la tecnología?, ya sea para mejorar el acceso a información científica de calidad, para aumentar las colecciones digitales, etc.
De esta manera visibilizar las falencias y los reales aportes que tuvieron las bibliotecas en Chile durante la pandemia del COVID-19
DOI: 10.5281/zenodo.7308180
title: dataset

description: something
DOI: 10.5281/zenodo.7308181
title: dataset

description: something
DOI: 10.5281/zenodo.7308280
title: dataset

description: something
DOI: 10.6084/m9.figshare.21266142
title: Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth dataset

description: This is a dataset of participants enrolled and followed in the CryptoResp sub-study looking at the association between respiratory and diarrhoea pathogens and growth in children 2-24 months.
DOI: 10.6084/m9.figshare.21266142.v1
title: Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth dataset

description: This is a dataset of participants enrolled and followed in the CryptoResp sub-study looking at the association between respiratory and diarrhoea pathogens and growth in children 2-24 months.
DOI: 10.6084/m9.figshare.21266142.v2
title: Respiratory and diarrhoeal pathogens in Malawian children hospitalised with diarrhoea and association with short-term growth dataset

description: This is a dataset of participants enrolled and followed in the CryptoResp sub-study looking at the association between respiratory and diarrhoea pathogens and growth in children 2-24 months.
DOI: 10.7924/r40867k2k
title: Data from: Structural instability and divergence from conserved residues underlie intracellular retention of mammalian odorant receptors

description: Mammalian odorant receptors are a diverse and rapidly evolving set of G protein-coupled receptors expressed in olfactory cilia membranes. Most odorant receptors show little to no cell surface expression in non-olfactory cells due to endoplasmic reticulum retention, which has slowed down biochemical studies. Here, we provide evidence that structural instability and divergence from conserved residues of individual odorant receptors underlie intracellular retention using a combination of large-scale screening of odorant receptors cell surface expression in heterologous cells, point mutations, structural modeling, and machine learning techniques. We demonstrate the importance of conserved residues by synthesizing "consensus" odorant receptors that show high levels of cell surface expression similar to conventional G protein-coupled receptors. Furthermore, we associate in silico structural instability with poor cell surface expression using molecular dynamics simulations. We propose an enhanced evolutionary capacitance of olfactory sensory neurons that enable the functional expression of odorant receptors with cryptic mutations.
DOI: 10.7924/r44f1tk8k
title: Data from: Beyond orphaned infants: novel effects of maternal death in wild primates

description: Primate offspring often depend on their mothers well beyond the age of weaning, and offspring that experience maternal death in early life can suffer substantial reductions in fitness across the lifespan. Here we leverage data from eight wild primate populations (seven species) to examine two underappreciated pathways linking early maternal death and offspring fitness that are distinct from direct effects of orphaning on offspring survival. First, we show that, for five of the seven species, offspring face reduced survival during the years immediately preceding maternal death, while the mother is still alive. Second, we identify an intergenerational effect of early maternal loss in three species (muriquis, baboons, and blue monkeys), such that early maternal death experienced in one generation leads to reduced offspring survival in the next. Our results have important implications for the evolution of slow life histories in primates, as they suggest that maternal condition and survival are more important for offspring fitness than previously realized.
DOI: 10.7924/r4kp82d1z
title: Data from: Genetic ancestry predicts male-female affiliation in a natural baboon hybrid zone

description: Opposite-sex social relationships are important predictors of fitness in many animals, including several group-living mammals. Consequently, understanding sources of variance in the tendency to form opposite-sex relationships is important for understanding social evolution. Genetic contributions are of particular interest due to their importance in long-term evolutionary change, but little is known about genetic effects on male-female relationships in social mammals, especially outside of the mating context. Here, we investigate the effects of genetic ancestry on male-female affiliative behavior in a hybrid zone between the yellow baboon (Papio cynocephalus) and the anubis baboon (P. anubis), in a population in which male-female social bonds are known predictors of lifespan. We place our analysis within the context of other social and demographic predictors of affiliative behavior in baboons. Genetic ancestry was the most consistent predictor of opposite-sex affiliative behavior we observed, with the exception of strong effects of dominance rank. Our results show that increased anubis genetic ancestry is associated with a subtle, but significantly higher probability of opposite-sex affiliative behavior, in both males and females. Additionally, pairs of anubis-like males and anubis-like females were the most likely to socially affiliate, resulting in moderate assortativity in grooming and proximity behavior as a function of genetic ancestry. Our findings indicate that opposite-sex affiliative behavior partially diverged during baboon evolution to differentiate yellow and anubis baboons, despite overall similarities in their social structures and mating systems. Further, they suggest that affiliative behavior may simultaneously promote and constrain baboon admixture, through additive and assortative effects of ancestry, respectively.
DOI: 10.7924/r46h4pk10
title: Data from: Computational 3D microscopy with optical coherence refraction tomography

description: This repository contains multi-angle volumetric OCT datasets for 3D OCRT reconstruction of four biological samples (zebrafish larva, fruit fly, mouse esophagus, and mouse trachea). The OCT volumes were captured using a custom spectral domain OCT system with a parabolic mirror in the sample arm, which facilitated multi-view capture over an ultra-wide angular range. For more details, see our accompanying publication, "Computational 3D microscopy with optical coherence refraction tomography" (https://doi.org/10.1364/OPTICA.454860). We have also included TensorFlow model checkpoint files that store the system precalibration parameters for each sample. These files are utilized in the accompanying 3D reconstruction python code, which can be found here https://github.com/kevinczhou/3d-ocrt. Please visit this link for further usage instructions.
DOI: 10.6084/m9.figshare.7472168
title: Skull-stripped Contrast-Enhanced MRI Datasets

description: Skull-stripped Contrast-Enhanced MRI Datasets. Please use the following citations if you use them in your work:<br> <br> L. Lindner, M. Kolodziej, J. Egger. Skull-stripped Contrast-Enhanced MRI Datasets. Figshare, 2018.<br> L. Lindner, et al. Using synthetic training data for deep learning-based GBM segmentation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6724-6729. IEEE, 2019. J. Egger, et al. GBM Volumetry using the 3D Slicer Medical Image Computing Platform. Sci Rep., 3:1364, 2013.<br> <br> Further Information:<br> <br> Ten Contrast-Enhanced T1-weighted Magnetic Resonance Imaging (MRI) Datasets of healthy Brains.<br> <br> All Datasets are skull-stripped and datasets with a sagittal or coronal scanning direction have been reformatted to an axial direction.<br> <br> Skull-stripping has been performed with BrainSuite.
DOI: 10.6084/m9.figshare.7435385
title: Skull-stripped MRI GBM Datasets (and Segmentations)

description: Skull-stripped MRI GBM Datasets. Please use the following citations if you use them in your work:<br> L. Lindner, D. Wild, M. Weber, M. Kolodziej, G. von Campe, J. Egger. Skull-stripped MRI GBM Datasets. Figshare, 2018. L. Lindner, et al. Using synthetic training data for deep learning-based GBM segmentation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6724-6729. IEEE, 2019. J. Egger, et al. GBM Volumetry using the 3D Slicer Medical Image Computing Platform. Sci Rep., 3:1364, 2013. <br> <br> Further Information:<br> <br> Ten Contrast-enhanced T1-Weighted MRI Datasets from Patients with pathologically confirmed Glioblastoma Multiforme (GBM) and Manual Expert Segmentations from Neurosurgeons.<br> <br> All Datasets are skull-stripped and reformatted to have 260 slices in axial direction. Datasets with a sagittal or coronal scanning direction have been reformatted to an axial direction.<br> <br> Note: Only the enhancing tumor and the necrotic core were segmented, since these are currently the areas of main interest in clinical practice. Other regions, such as a surrounding edema, are typically not delineated in clinical routine, since these annotations do often not accurately reflect the true state.<br>
DOI: 10.17632/fccyrx6zs3.1
title: Mendeley supplemental materials for a manuscript entitled "Digital cognitive behavior therapy improves outcomes of atopic dermatitis"

description: Supplemental tables
Mendeley supplemental table 1. Forty neurotransmitters and associated metabolites measured by liquid chromatography tandem mass spectrometry.
Mendeley supplemental table 2. Clinical details of patients with AD enrolled for assessment of negative emotions.
Mendeley supplemental table 3. Demographic characteristics of healthy controls and patients with AD that were tested for neurotransmitters.
DOI: 10.6084/m9.figshare.21525939
title: Dataset

description: This set includes all collected data and figures used in this paper.
DOI: 10.34740/kaggle/dsv/4477395
title: Mahanagar Yatayat Dataset
DOI: 10.17632/xsx6wv435b.1
title: The RNA-RNA interactome between a phage and its satellite virus reveals a small RNA differentially regulates gene expression across both genomes Dunham et al 

description: Replicates and uncropped gels for Northern and Western blots in manuscript.
DOI: 10.6084/m9.figshare.21525966
title: Numerical data for main figures (Algamal et al 2022)

description: Numerical data for all panels of the main figures of Algamal et al 2022 accepted in Communications Biology
DOI: 10.6084/m9.figshare.21525966.v1
title: Numerical data for main figures (Algamal et al 2022)

description: Numerical data for all panels of the main figures of Algamal et al 2022 accepted in Communications Biology
DOI: 10.5258/soton/399704
title: Vectrino + ADV files, trial data and supplementary information
title:  Experimental Data for Research: Assessing hydrodynamic space use of brown trout, Salmo trutta, in a complex flow environment: A return to first principles

description: Vectrino + Acoustic Doppler Velocimetry files for each treatment (.vno files), trial data for all fish (.xlsx) and supplementary information to go with main manuscript (.docx).

Dataset to support:
Kerr, James et al (2016) Assessing hydrodynamic space use of brown trout, Salmo trutta, in a complex flow environment: A return to first principles. Journal of Experimental Biology
DOI: 10.6084/m9.figshare.21406866.v4
title: raw data

description: Nothing
DOI: 10.25422/azu.data.20113466.v1
title: Data for "Controls on determining the bulk water content of the Moon"

description: SPICES and alphaMELTS input and output files to simulate lunar magma ocean crystallization by varying bulk H<sub>2</sub>O, D<sub>H</sub>, interstitial melt fraction and initial magma ocean depth. <em></em> <em><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i></em>
DOI: 10.25422/azu.data.20113466
title: Data for "Controls on determining the bulk water content of the Moon"

description: SPICES and alphaMELTS input and output files to simulate lunar magma ocean crystallization by varying bulk H<sub>2</sub>O, D<sub>H</sub>, interstitial melt fraction and initial magma ocean depth. <em></em> <em><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i></em>
DOI: 10.23645/epacomptox.21526032
title: Chemistry Dashboard Data: Abstract Sifter

description: All files for the version 5.5 release of the Abstract Sifter tool to the CompTox Chemicals Dashboard. <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.23645/epacomptox.21526032.v2
title: Chemistry Dashboard Data: Abstract Sifter

description: All files for the version 5.5 release of the Abstract Sifter tool to the CompTox Chemicals Dashboard. <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.23645/epacomptox.21526032.v1
title: CompTox Chemicals Dashboard: Abstract Sifter

description: All files for the version 5.5 release of the Abstract Sifter tool to the CompTox Chemicals Dashboard. <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.5281/zenodo.7283277
title: Dataset from Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience.

description: Dataset from the paper: Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience. Each variable is described in Description.pdf. Analysis code is available at https://github.com/HiroshiMakinoLaboratory/Makino2022NatureNeuroscience.
DOI: 10.5281/zenodo.7283276
title: Dataset from Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience.

description: Dataset from the paper: Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience. Each variable is described in Description.pdf. Analysis code is available at https://github.com/HiroshiMakinoLaboratory/Makino2022NatureNeuroscience.
DOI: 10.15139/s3/ogcu6b
title: Vision Zero Action Plan (2022). Apex, North Carolina

description: Vision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context.
DOI: 10.34740/kaggle/dsv/4325150
title: Scrabble data from woogles.io
DOI: 10.5517/ccdc.csd.cc11fb1v
title: CCDC 966116: Experimental Crystal Structure Determination

description: Related Article: Sara B. Honorato, Katarina B. Saraiva, J. Ellena, Alejandro P. Ayala, Juan C. Tenorio|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25wnb2
title: CCDC 2022574: Experimental Crystal Structure Determination

description: Related Article: Yunsheng Wang|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27463q
title: CCDC 2059612: Experimental Crystal Structure Determination

description: Related Article: Gustav J. Wørmer, Nikolaj L. Villadsen, Peter Nørby, Thomas B. Poulsen|2021|Angew.Chem.,Int.Ed.|60|10521|doi:10.1002/anie.202101439
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc276mcg
title: CCDC 2061945: Experimental Crystal Structure Determination

description: Related Article: Gustav J. Wørmer, Nikolaj L. Villadsen, Peter Nørby, Thomas B. Poulsen|2021|Angew.Chem.,Int.Ed.|60|10521|doi:10.1002/anie.202101439
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28xsss
title: CCDC 2113046: Experimental Crystal Structure Determination

description: Related Article: Jaya Mehara, Brandon T. Watson, Anurag Noonikara-Poyil, Adway O. Zacharias, Jana Roithová, H. V. Rasika Dias|2022|Chem.-Eur.J.|28|e202103984|doi:10.1002/chem.202103984
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.