cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6385536
title: Iphones publicados en la página web de Back Market el 25/03/2022 extraídos con Web Scraping

description: El dataset recoge todos los Iphone en venta a la página web de Back Market el día 25/03/2022 a las 19:56, correspondiendo a una extracción con Web Scraping de esta plataforma online, que se dedica a vender productos reacondicionados. El conjunto de datos incluye todos los Iphone disponibles en la tienda con sus características principales: nombre del producto (<em>Producte</em>), su capacidad en Gigas (<em>Capacitat</em>), su color (<em>Color</em>), si es libre de operador (<em>Operador</em>), el precio del producto (<em>Preu</em>), la puntuación del producto (<em>Puntuacio</em>), la empresa que ha reacondicionado el producto (<em>Reacondicionador</em>), desde donde se envía el producto (<em>Origen_enviament</em>), los meses de garantía (<em>Garantia</em>) y la url de cada producto (<em>Url</em>).
DOI: 10.5281/zenodo.6385534
title: Iphones publicados en la página web de Back Market el 25/03/2022 extraídos con Web Scraping

description: El dataset recoge todos los Iphone en venta a la página web de Back Market el día 25/03/2022 a las 19:56, correspondiendo a una extracción con Web Scraping de esta plataforma online, que se dedica a vender productos reacondicionados. El conjunto de datos incluye todos los Iphone disponibles en la tienda con sus características principales: nombre del producto (<em>Producte</em>), su capacidad en Gigas (<em>Capacitat</em>), su color (<em>Color</em>), si es libre de operador (<em>Operador</em>), el precio del producto (<em>Preu</em>), la puntuación del producto (<em>Puntuacio</em>), la empresa que ha reacondicionado el producto (<em>Reacondicionador</em>), desde donde se envía el producto (<em>Origen_enviament</em>), los meses de garantía (<em>Garantia</em>) y la url de cada producto (<em>Url</em>).
DOI: 10.5281/zenodo.5815131
title: Variables from several snapshots of 0% net helicity driven sunspot simulation

description: The included dataset is from an magnetohydrodynamic simulation of a sunspot that is driven by photospheric motions. Three snapshots in time are extracted, and the magnetic field, velocity, and current from those snapshots is stored in the attached dataset, along with the three coordinate arrays. The datafiles are in HDF5 format, which is easily read in all major programming languages. The simulation is largely described in https://ui.adsabs.harvard.edu/abs/2017ApJ...851L..17K/abstract
DOI: 10.48511/f6aw-1j08
title: Lizard Creek (LIZ005)

description: This program tracks temperature at 1 hour intervals. Over time, the data can be used to understand the effects of climate change on the Elk River and its tributaries.
DOI: 10.26078/c9sz-m139
title: Plant-soil Feedbacks Help Explain Plant Community Productivity
DOI: 10.5281/zenodo.6385566
title: Variables from several snapshots of 0% net helicity driven sunspot simulation

description: The included dataset is from an magnetohydrodynamic simulation of a sunspot that is driven by photospheric motions. Three snapshots in time are extracted, and the magnetic field, velocity, and current from those snapshots is stored in the attached dataset, along with the three coordinate arrays. The datafiles are in HDF5 format, which is easily read in all major programming languages. The simulation is largely described in https://ui.adsabs.harvard.edu/abs/2017ApJ...851L..17K/abstract. The files are read directly from the restart files and the variables reside at their original grid locations. The files labeled fieldline.* are fortran unformatted datasets of magnetic field lines traced from the boundary of the simulation at these time steps. The dimensions of the traced field lines are 401x401x400x3.
DOI: 10.5281/zenodo.6385593
title: Variables from several snapshots of 0% net helicity driven sunspot simulation

description: The included dataset is from an magnetohydrodynamic simulation of a sunspot that is driven by photospheric motions. Three snapshots in time are extracted, and the magnetic field, velocity, and current from those snapshots is stored in the attached dataset, along with the three coordinate arrays. The datafiles are in HDF5 format, which is easily read in all major programming languages. The simulation is largely described in https://ui.adsabs.harvard.edu/abs/2017ApJ...851L..17K/abstract. The files are read directly from the restart files and the variables reside at their original grid locations. The files labeled fieldline.* are fortran unformatted datasets of magnetic field lines traced from the boundary of the simulation at these time steps. The dimensions of the traced field lines are 401x401x400x3.
DOI: 10.6092/unibo/amsacta/6876
title: Booster Dataset

description: We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.
DOI: 10.6092/unibo/amsacta/6877
title: RGB-MS Dataset

description: We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotated with semi-dense, high-resolution ground-truth labels in the form of disparity maps. To tackle the task, we propose a deep learning architecture trained in a self-supervised manner by exploiting a further RGB camera required only during training data acquisition. In this setup, we can conveniently learn cross-modal matching in the absence of ground-truth labels by distilling knowledge from an easier RGB-RGB matching task based on a collection of about 11K unlabeled image triplets. Experiments show that the proposed pipeline sets a good performance bar (1.16 pixels average registration error) for future research on this novel, challenging task.
DOI: 10.7303/syn26345526
title: fastmri-plus
DOI: 10.25625/sanzcz
title: Replication data for: Robust Finite Element Discretizations for a PDE arising in Helioseismology

description: Contains the corresponding Code for the numerical experiments in my thesis. In the file "solvers.py", you can find different discretization schemes related to the model problem discussed in the thesis, the file "simulations.py" was used to generate the data discussed in the thesis and shows how to use the "solvers.py" file
DOI: 10.5281/zenodo.6385646
title: Dataset: Multi-Stakeholder Validation of Entrustable Professional Activities for a Family Medicine Care of the Elderly Residency Program: A Focus Group Study

description: ZOOM Meeting transcripts from 5 stakeholder group meetings used in the study.
DOI: 10.5281/zenodo.6385645
title: Dataset: Multi-Stakeholder Validation of Entrustable Professional Activities for a Family Medicine Care of the Elderly Residency Program: A Focus Group Study

description: ZOOM Meeting transcripts from 5 stakeholder group meetings used in the study.
DOI: 10.6084/m9.figshare.19230150
title: A. pittii MAGs from MT1 environmental metagenomes

description: Assembled A. pittii MAGs from ISS metagenomes.
DOI: 10.6084/m9.figshare.19230150.v1
title: A. pittii MAGs from MT1 environmental metagenomes

description: Assembled A. pittii MAGs from ISS metagenomes.
DOI: 10.6084/m9.figshare.19230162
title: A. pittii gene catalogs

description: Gene catalogs from A. pittii isolates
DOI: 10.6084/m9.figshare.19230162.v1
title: A. pittii gene catalogs

description: Gene catalogs from A. pittii isolates
DOI: 10.6084/m9.figshare.19230165
title: A. pittii functional annotations

description: Functional annotations from prokka for genes predicted from A. pittii genomes.
DOI: 10.6084/m9.figshare.19230165.v1
title: A. pittii functional annotations

description: Functional annotations from prokka for genes predicted from A. pittii genomes.
DOI: 10.13012/b2idb-2692533_v2
title: Burton Endo electron micrograph library

description: The data are original electron micrographs from the lab of the late Dr. Burt Endo of the USDA. These data were digitized from photographic prints and glass plate negatives at 600 DPI as 16 bit TIFF files. This second version added 10 new ZIP files from the Endo data collection. "Endo folder database.xlsx" is updated to reflect the addition. Information in "ReadmeFile name formatting.docx" remains the same as in V1.
DOI: 10.6084/m9.figshare.19424255
title: Summary Data for PVA Stiffness Manuscript

description: The quantitative data used for analysis in the PVA stiffness manuscript. Might be used to confirm quantitative relationships or compare or reanalyze the data for a different paper or application.
DOI: 10.5281/zenodo.6385654
title: Gut metabolites predict Clostridioides difficile recurrence

description: This repository contains: 1. Data used for analyses from: A) 16S rRNA gene amplicon analysis after paired-end Fastq files were truncated, filtered, denoised, and merged ["1-ASV_Counts.xlsx"] B) Untargeted metabolomics as provided by Metabolon ["2-UntargetedMetabolomics.xlsx"] C) Precision SCFA analysis ["3-PrecisionSCFA.xlsx"] D) Clincal demographic data of the study cohort ["Clinical Demographics.csv"] 2. Detailed results of univariate analyses in sub-folder "Univariate analysis results" for clinical data, untargeted metabolomics data, SCFAs, and ASVs 3. Detailed results of predictive analysis in subfolder "Predictive Analyses results", including per-fold performance results ("9-PredictiveResults.xlsx") and analysis of predictive features for ASVs ("10-ASVsPredictiveAnalysis.xlsx"), untargeted metabolites ("11-MetabsPredictiveAnalysis.xlsx"), targeted SCFAs ("12-SCFAPredictive Analysis.xlsx"), clinical data ("13-DemoPredictiveAnalysis.xlsx") and all data ("14-AllDataPredictiveAnalysis.xlsx")
DOI: 10.6084/m9.figshare.19310543
title: Analyzed dataset

description: Datasets from our manuscript "Functional response to a microbial synbiotic in the gastrointestinal system of children"<br>
DOI: 10.6084/m9.figshare.19310543.v1
title: Analyzed dataset

description: Datasets from our manuscript "Functional response to a microbial synbiotic in the gastrointestinal system of children"<br>
DOI: 10.22003/xfel.eu-data-003221-00
title: RELAX setup beamtime
DOI: 10.17632/ck9gd5vbkr.1
title: PNG highland tephras

description: Descriptions and maps of Late Pleistocene tephras in the highlands of Papua New Guinea
DOI: 10.17632/98x5pkw364.1
title: Table S1. Genotype calls based on the Gifu reference assembly

description: Mustamin et al., 2022. New Phytologist Supporting Information
DOI: 10.6084/m9.figshare.16620238.v3
title: A public dataset on long-distance running training in 2019 and 2020

description: This dataset contains 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet.<br>The data with the athletes' activities are contained in dataframe objects (tabular data) and saved in the Parquet file format using the Pandas library, part of the Python ecosystem for data science. Each Pandas dataframe contains the following data (as different columns) for each athlete (as different rows), the first word identifies the name of the column in the dataframe:- datetime: date of the running activity;- athlete: a computer-generated ID for the athlete (integer);- distance: distance of running (floating-point number, in kilometers);- duration: duration of running (floating-point number, in minutes);- gender: gender (string 'M' of 'F');- age_group: age interval (one of the strings '18 - 34', '35 - 54', or '55 +');- country: country of origin of the athlete (string);- major: marathon(s) and year(s) the athlete ran (comma-separated list of strings).<br>For convenience, we created files with the athletes' activities data sampled at different frequencies: day 'd', week 'w', month 'm', and quarter 'q' (i.e., there are files with the distance and duration of running accumulated at each day, week, month, and quarter) for each year, 2019 and 2020. Accordingly, the files are named 'run_ww_yyyy_f.parquet', where 'yyyy' is '2019' or '2020' and 'f' is 'd', 'w', 'm' or 'q' (without quotes). The dataset also contains data with different government’s stringency indexes for the COVID-19 pandemic. These data are saved as text files and were obtained from https://ourworldindata.org/covid-stringency-index. The Jupyter notebooks that we created and made available in the https://github.com/BMClab/covid19 repository exemplify the use of the data.
DOI: 10.17632/47sm4njrbt.1
title: Test-repo-jrbt

description: Just a test run to see how Mendeley works.
DOI: 10.17632/fns4dbrd7y.1
title: Supplementary figures and tables

description: Figure legends
Supplementary Figure 1. Effects of (A) ATAD2B, (B) AGFG2, (C) MTMR2, (D) FAM13B, (E) CD2BP2, (F) AP5M1, (G) SHOC2, (H) ZKSCAN5 or (I) CNOT4 knockdown on cell growth.

Supplementary table 1. Characteristics of laryngeal squamous cell carcinoma patients categorized by immunoreactive score (IRS) of HMGB1 and LSM6.

Supplementary table 2. The gene sequences for shRNA target

Supplementary Table 3. Differentially expressed genes in Hep2 cell transfected by si-HMGB1.
DOI: 10.17632/x75wfd3dch.2
title: Supplementary Material For: Informal Labor Markets in Times of Pandemic

description: This compressed file contains all the supporting files to replicate tables and figures in our paper. Please refer to the readme.pdf file for further instructions and cite this material accordingly.
DOI: 10.17632/nvgjcn5nsx.1
title: Leaf and needle spectra for 25 boreal tree species

description: This dataset contains reflectance and transmittance spectra of leaf and needle samples collected from trees grown in four sites in the Helsinki area in Southern Finland: Otaniemi campus (60°11´N, 24°49´E), Viikki arboretum (60°13´N, 25°00´E), Kumpula botanical garden (60°12´N, 24°57´E), and Ruotsinkylä research forest (60°22´N, 24°59´E). 

The spectra have been reported in Hovi, A., Raitio, P., Rautiainen, M., (2017). A spectral analysis of 25 boreal tree species. Silva Fennica, 51(4), 7753, https://doi.org/10.14214/sf.7753. If using the data, please cite the above publication and the Mendeley Dataset: Hovi, A., Rautiainen, M., (2022). Leaf and needle spectra for 25 boreal tree species. Mendeley Data, V1, https://doi.org/10.17632/nvgjcn5nsx.1.

Reflectance and transmittance spectra were measured for a total of 25 tree species close to peak growing season in 2016. In addition to one-time measurements of the 25 species, the dataset contains time series of spectra for the dominating tree species in Finland (Betula pendula, Picea abies, Pinus sylvestris, Populus tremula) from May to October. The number of sampled trees varied from 1 to 3 per species. For each tree, three leaf or needle samples from top part of the tree crown (predominantly sun-exposed foliage) and three samples from bottom part of the tree crown (predominantly shaded foliage) were measured. Both sides of the sample (A = adaxial = upper, B = abaxial = lower) were measured with the following exceptions. Needles of spruce species (Picea abies, Picea glauca, Picea mariana) do not have clearly defined sides, and therefore only one side of the sample (marked as A in the data) was measured. For tamarack (Larix laricina), both sides were measured, but it was not possible to reliably distinguish which is upper and which is lower.

The following additional data were not reported in Hovi et al. (2017), but are included in the dataset for the sake of thoroughness:
    i) The measurements of Picea abies and Pinus sylvestris made between 22nd Jul and 1st Aug 2016 employed slightly different needle carriers than the other measurements. This could have affected the comparability of these samples with the others, and these samples should be used with caution.
    ii)	For Picea glauca, one tree (species labeled as PICGLAA in the data) was sampled that represents a long-needled form of P. glauca (P. glauca var. albertiana (S. Br.) Sarg.).

For further details of the data collection, measurements, and the content of the data files, see Hovi et al. (2017) and README.pdf.
DOI: 10.17632/t5f554s7cn.1
title: Tree leaf and needle spectra for three sites in northern and central Europe

description: This dataset contains reflectance and transmittance spectra of leaf and needle samples, collected near peak growing season in 2019 and 2020, from three study sites in northern and central Europe: Hyytiälä (61°51'N, 24°18'E, Finland), Järvselja (58°17'N, 27°19'E, Estonia), and Bílý Kříž (49°30'N, 18°32'E, the Czech Republic).

The spectra have been reported in Hovi, A., Lukeš, P., Homolová, L., Juola, J., Rautiainen, M. (2022a). Small geographical variability observed in Norway spruce needle spectra across Europe. Silva Fennica vol. 56 no. 2 article id 10683. https://doi.org/10.14214/sf.10683. If using the data, please cite the above publication and the Mendeley Dataset: Hovi, A., Juola, J., Lukeš, P., Homolová, L., Rautiainen, M. (2022b). Tree leaf and needle spectra for three sites in northern and central Europe. Mendeley Data, V1, https://doi.org/10.17632/t5f554s7cn.1.

For the following species and study sites, we collected samples from three individual trees per study site. For each tree, we measured three samples of top-of-canopy (predominantly sun-exposed) and three samples of bottom-of-canopy (predominantly shaded) foliage. For evergreen conifers, we sampled current-year and one-year-old needles, which doubled the number of samples. For each tree, characteristics (height, diameter) of the tree and the surrounding forest stand were recorded.

    Norway spruce (Picea abies (L.) H. Karst), all sites
    Scots pine (Pinus sylvestris L.), Hyytiälä/Järvselja
    silver birch (Betula pendula Roth), Hyytiälä/Järvselja
    black alder (Alnus glutinosa (L.) Gaertn.), Järvselja
    European aspen (Populus tremula L.), Järvselja

The spectra of the following five rare species in the Järvselja site were also measured. These data were not reported in Hovi et al. (2022a) but are included here for the sake of thoroughness. For these species, we sampled one tree per species and only top-of-canopy foliage. Three samples per each tree were collected. These trees were all young (up to 5 m in height). No measurements of the tree or stand characteristics were made.

    American hazelnut (Corylus avellana L.)
    small-leaved linden (Tilia cordata Mill.)
    goat willow (Salix caprea L.)
    European ash (Fraxinus Excelsior L.)
    other willow, species unknown (Salix sp.)

For further details of the data collection, measurements, and the content of the data files, see Hovi et al. (2022a) and README.pdf.
DOI: 10.17632/crb5tkbvpb.3
title: ROI extraction of chlorophyll Fluorescence wheat canopy images using  novel Curve Fit Based K- means segmentation Algorithm for automatic drought stress detection

description: The data consist of a file system-based data of Raj 3765  variety of wheat. There are twenty-four chlorophyll fluorescence images every day for each  (Control and Drought) have been captured for a period of sixty days. A total of (1440 x 2) images are in used  for this research work.

Created dataset is subjected to a novel segmentation algorithms called "Cfit k-means " to extract appropriate ROI.
Experimentation include analysis of seven segementation algorithms as follows:
1. Global Static Thresholding
2. Global K-means Thesholding
3. Otsu Thresholding
4. K-means 
5. Meanshift
6. Watershed
7. Cfit K-means Segmenetation

Results of the segmentation Process shared below for both control and drought experiments.
DOI: 10.17632/6ncwmyx6y4.1
title: Dataset on the determinants of willingness to continuously use financial technology among Indonesian university students

description: The dataset examines the two factors of perceived benefit and perceived risk that influence millennials Indonesian university student’ willingness to continuously continuance use financial technology.
DOI: 10.17632/bvpvk39svk.1
title: Titanium nanospikes activate macrophage phagocytosis by ligand-independent contact stimulation

description: Raw and processed data
DOI: 10.17632/trd43wp68c.2
title: List of macrozoobenthos groups and species in model groups (present / absent) in the shallow tundra lakes (North-east of the European part of Russia) 

description: We studied biodiversity of zoobenthos communities in 2000-2014 in the shallow tundra lakes (North-east of the European part of Russia)
This table presents the list of macrozoobenthos taxa from samples collected in the Malozemelskaya and Bolshezemelskaya tundra. The list includes revealed taxonomic groups of zoobenthos (16) and lower-rank taxa (83) for some (model) groups: Oligochaeta, Hirudinea, Mollusca, Phyllopoda, Amphipoda, Ephemeroptera, Plecoptera, Coleoptera, Trichoptera. 

Each column of the table corresponds to a group of shallow lakes in different river basins of tundra (I-VI): I – lakes from Ortina River Basin; II – lakes from Neruta River Basin; III – lakes from Kolva River Basin; IV – lakes from More-yu River Basin; V – lakes near Syattey-ty lakes (Vork); VI – Yarey-yu River Basin. 
The list of taxa contains only our own data. 

1. Chertoprud M.V., Chertoprud E.S. A short guide to freshwater invertebrates in the center of European Russia. M.: KMK Scientific Press Ltd., 2010.184 p. (In Russian)
2. Key to Identification of Zooplankton and Zoobenthos of Fresh Water of European Russia, KMK Scientific Press, Moscow, 2010, pp. 151–183. (In Russian).
3. Lillehammer A. 1988. Stoneflies (Plecoptera) of Fennoscandian and Denmark. Fauna Entomologica Scandinavica Vol. 21., Leiden, 169 pp. 
4. Teslenko V.A., Zhiltzova, L.A. Key to the stoneflies (Insecta, Plecoptera) of Russia and adjacent countries. Imagines and nymphs Dalnauka, Vladivostok, 2009. 382 pp. [in Russian].
5. Lepneva S. G. Fauna of the USSR. Caddisflies. Vol. II. Issues 1, 2. Zoological Institute of the USSR Academy of Sciences. Moscow, Leningrad. 1964. 560 pp. [in Russian]. 
6. Timm T. A guide to the freshwater Oligochaeta and Polychaeta of northern and central Europe. Lauterbornia, 1999. Issues 66.  235 pp.
7. Methodology for the study of biogeocenoses of inland waters.  M .: Nauka, 1975. 240 pp. (in Russian).

The study was performed in part as Federal Tasks of Department of Animals Ecology of the Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences “Faunal diversity and spacial and ecological structure of animal communities in the North-East of European Russia and adjacent areas in the context of environmental changes and development" (№ 1021051101423-9-1.6.12;1.6.13;1.6.14)