cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4463468
title: Data from: A sink host allows a specialist herbivore to persist in a seasonal source

description: <strong>Filename: </strong>1_Population_growth_rate.xlsx Variables: 1. Source host - the plant species from which experimental females were transferred <br> 2. Target host - the plant species to which experimental females were transferred<br> 3. Generations - tested time period, in generations<br> 4. N<sub>0</sub> - the number of females placed at the beginning of the experiment<br> 5. N - the number of mites (being a progeny of N<sub>0</sub> females) counted after each tested time period <strong>Filename: </strong>2_Emigration_dispersal.xlsx Variables: 1. Source host - the plant species infested by mites and exposed to wind<br> 2. Target host - the plant species toward which mites could disperse<br> 3. N - population size on the source host<br> 4. D - the number of individuals that dispersed from the source host <strong>Filename: </strong>3_Emigration_acceptance.xlsx Variables: 1. Source host - the plant species from which experimental females were transferred <br> 2. Target host - the plant species to which experimental females were transferred<br> 3. N - the number of females placed on the experimental arena<br> 4. R - the number of females that stayed on the experimental arena after incubation <strong>Filename: </strong>4_Experimental_evolution.xlsx Variables: 1. Regime - host selection regime (W - wheat; B - brome; WB - wheat-brome alternating each three generations on each host species)<br> 2. Generations - the number of generations the population survived<br> 3. Status - 0 - censored observation; 1 - observed event of extinction <strong>Filename: </strong>5_Field_database.xlsx Variables: 1. Sampling date - date of plant collection in the field<br> 2. Day - the day of the year when the sample was collected<br> 3. Year - the year of the sample collection<br> 4. Database - N: the sample collected during 2012-2014 surveys; O: the sample collected during 2007-2014 surveys<br> 5. Host - the plant species collected<br> 6. GPS lat. [N] - the latitude in the northern hemisphere<br> 7. GPS long. [E] - the longitude in the eastern hemisphere<br> 8. n - the number of all plant shoots examined<br> 9. k - the number of plant shoots infested
description: The study was supported by the National Science Centre, Poland (NSC); research grant no. 2017/27/N/NZ8/00305 (tasks: assessing the population growth rate and host acceptance; experimental evolution); NSC research grant no. 2016/21/B/NZ8/00786 (tasks: establishing the stock population - including field work, barcoding and maintaining; assessing the dispersal rate). AL scholarships were funded by European Social Funds POWR.03.02.00–00–I006/17, NSC (PhD scholarship no. 2019/32/T/NZ8/00151) and Adam Mickiewicz University Foundation (awarded in 2019/2020). LK was involved in this work while supported by the NSC grant 2018/29/B/NZ8/00066. SMe became involved in this work while supported by a US Fulbright Scholarship.
DOI: 10.6084/m9.figshare.14039714.v2
title: Figure 4 Penicillin reconstruction

description: Data for Figure 4 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6084/m9.figshare.14039714
title: Figure 4 Penicillin reconstruction

description: Data for Figure 4 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6084/m9.figshare.14039714.v1
title: Figure 5 Penicillin reconstruction

description: Data for Figure 5 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6073/pasta/14189754d2a54c4c825fcd2ca3dbaad6
title: Eddy Covariance and Meteorological Data in the Clearcut Site at Harvard Forest 2009-2012

description: Clearcutting and other forest disturbances perturb carbon, water, and energy balances in significant ways, with corresponding influences on Earth’s climate system through biogeochemical and biogeophysical effects. Observations are needed to quantify the precise changes in these balances as they vary across diverse disturbances of different types, severities, and in various climate and ecosystem type settings. This dataset reports eddy covariance and micrometeorological measurements of surface-atmosphere exchanges that can be combined with related datasets from vegetation inventories and chamber-based estimates of soil respiration and leaf gas exchange to collectively quantify and understand how carbon, water, and energy fluxes changed during the first four years following forest clearing in a temperate forest environment of the northeastern US. Associated publications show rapid recovery with sustained increases in gross ecosystem productivity (GEP) over the first three growing seasons post-clearing, coincident with large and relatively stable net emission of CO2 because of overwhelmingly large ecosystem respiration. The rise in GEP was attributed to vegetation changes not environmental conditions (e.g. weather), but attribution to the expansion of leaf area versus changes in vegetation composition remains unclear. Soil respiration was estimated to contribute 44% of total ecosystem respiration during summer months and coarse woody debris accounted for another 18%. Evapotranspiration also recovered rapidly and continued to rise across years with a corresponding decrease in sensible heat flux. Gross shortwave and longwave radiative fluxes were stable across years except for strong wintertime dependence on snow covered conditions and corresponding variation in albedo. Overall, these findings underscore the highly dynamic nature of carbon and water exchanges and vegetation composition during the regrowth following a severe forest disturbance, and sheds light on both the magnitude of such changes and the underlying mechanisms with a unique example from a temperate, deciduous broadleaf forest.
DOI: 10.15468/bjxgxh
title: Studies on Neotropical crickets: The continental Otteiini taxa (Orthoptera Phalangopsidae), those cave crickets are not confined to the West Indies

description: This dataset contains the digitized treatments in Plazi based on the original journal article Cadena-Castañeda, Oscar J., Soto, Wolfang Andrés Rodríguez, Cárdenas, Andrea Del Pilar Floréz, Acevedo, Angélica (2021): Studies on Neotropical crickets: The continental Otteiini taxa (Orthoptera Phalangopsidae), those cave crickets are not confined to the West Indies. Zootaxa 4981 (2): 331-356, DOI: https://doi.org/10.11646/zootaxa.4981.2.7
DOI: 10.15468/cj6x9s
title: Description of the larva of Micrathyria paulsoni González-Soriano, 2020 (Odonata Libellulidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Novelo-Gutiérrez, Rodolfo, Arce-Pérez, Roberto (2021): Description of the larva of Micrathyria paulsoni González-Soriano, 2020 (Odonata Libellulidae). Zootaxa 4981 (2): 357-364, DOI: https://doi.org/10.11646/zootaxa.4981.2.8
DOI: 10.5281/zenodo.5031899
title: Characterizing ferromagnetic domains in ring structures using in-situ magnetic Fresnel imaging

description: This deposit contains supplementary datasets and data processing scripts used in a Specialization Project by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU). <strong>Dataset</strong>: 2021_03_26_FA721_A6.5_in_situ_stack.hspy Sample FA721, window W1, ring A6.5um, objective lens 0768.<br> Tilting range [-2.0, 2.0] deg. in X and [-2.0, 2.0] deg. in Y, step size 1.0 deg. <strong>Python files:</strong><br> processing.py utils.py p001_make_hyperspy.py Running processing.py produces the domains and areas as numpy files, that can be used for visualisation and quantifications.<br> Looping through the whole dataset takes quite some time, hence the results are also to be found in the .npy files. # Numpy files<br> areas.npy domains.npy <strong>Notebook:</strong> Jupyter_notebook.ipynb<br> More detail overlook of the algorithm, with visualizations and result
DOI: 10.6084/m9.figshare.14039792
title: Figure 5: Expansion of macrolide resistant German isolates

description: Source data for Figure 5 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6084/m9.figshare.14039792.v2
title: Figure 5: Expansion of macrolide resistant German isolates

description: Source data for Figure 5 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6084/m9.figshare.14039792.v1
title: Figure 6: Expansion of macrolide resistant German isolates

description: Source data for Figure 6 and associated supplementary figures in the elife submission: The role of interspecies recombinations in the evolution of antibiotic resistant pneumococci.
DOI: 10.6084/m9.figshare.14511861
title: Will the chemical probes please stand up?

description: Full data set used for the comparative study of chemical probes sources and their scoring schemes: Will the chemical probes please stand up?
DOI: 10.6084/m9.figshare.14511861.v2
title: Will the chemical probes please stand up?

description: Full data set used for the comparative study of chemical probes sources and their scoring schemes: Will the chemical probes please stand up?
DOI: 10.6084/m9.figshare.14511861.v1
title: Will the chemical probes please stand up?

description: Full data set used for the comparative study of chemical probes sources and their scoring schemes: Will the chemical probes please stand up?
DOI: 10.6084/m9.figshare.14702172
title: Cointegrate plasmid pSL131_IncA/C_IncX3 with different structures

description: Basic information of pSL131_IncA/C_IncX3 with different structures.
DOI: 10.6084/m9.figshare.14702172.v2
title: Cointegrate plasmid pSL131_IncA/C_IncX3 with different structures

description: Basic information of pSL131_IncA/C_IncX3 with different structures.
DOI: 10.6084/m9.figshare.14702172.v1
title: Cointegrate plasmid pSL131_IncA/C_IncX3 with different structures

description: Basic information of pSL131_IncA/C_IncX3 with different structures.
DOI: 10.6084/m9.figshare.14885115.v1
title: 基于TMT定量蛋白质组学筛选胸主动脉瘤_夹层蛋白质标志物_张煜.pdf

description: date
DOI: 10.5281/zenodo.5039223
title: ReaLSAT, a global dataset of reservoir and lake surface area variations

description: Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2015) of 683734 water bodies below 50°N and size between 0.1 to 100 square kilometers. The dataset contains the following files: 1) ReaLSAT.zip: A shapefile that contains reference shape of waterbodies in the dataset. 2) monthly_timeseries.zip: contains one csv file for each water body. The CSV file provides monthly surface area variation values. The csv files are stored in subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contains csv files of lake that lie between 60 E and 70 E longitude, and 50S and 40 S. 3) monthly_shapes_&lt;bottom_left_lon&gt;_&lt;bottom_left_lat&gt;.zip: contains shapefile for each water body that lie within the 10 degree by 10 degree cell. The shapefile provides monthly shapes together with their time and area attribute. 4) ReaLSAT.html: a readme python notebook that provides information about reading and visualizing the dataset. The notebook also contains the code to download the data to reduce the overhead of downloading each file manually. 5) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data. Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset: [1] Khandelwal, Ankush, Rahul Ghosh, Zhihao Wei, Huangying Kuang, Hilary Dugan, Paul Hanson, Anuj Karpatne, and Vipin Kumar. "ReaLSAT: A new Reservoir and Lake Surface Area Timeseries Dataset created using machine learning and satellite imagery." (2020). [2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).
DOI: 10.25387/g3.14798814
title: Supplemental Material for Jackson et al., 2021

description: Supplemental Material for Jackson et al., 2021<br>
DOI: 10.25387/g3.14798814.v1
title: Supplemental Material for Jackson et al., 2021

description: Supplemental Material for Jackson et al., 2021<br>
DOI: 10.18112/openneuro.ds003465.v1.0.4
title: DMCC55B
DOI: 10.5281/zenodo.5046076
title: UniToBrain Dataset

description: The University of Turin (UniTO) released the open-access dataset UniTOBrain collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP). The dataset includes 100 training subjects and 15 testing subjects used in a submitted publication for the training and the testing of a Convolutional Neural Network (CNN, see for details: https://arxiv.org/abs/2101.05992, https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model, https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1). At this stage, the UniTO team released this dataset privately, but soon it will be public. This is a subsample of a greater dataset of 258 subjects that will be soon available for download at https://ieee-dataport.org/.<br> CTP data from 258 consecutive patients were retrospectively obtained from the hospital PACS of Città della Salute e della Scienza di Torino (Molinette). CTP acquisition parameters were as follows: Scanner GE, 64 slices, 80 kV, 150 mAs, 44.5 sec duration, 89 volumes (40 mm axial coverage), injection of 40 ml of Iodine contrast agent (300 mg/ml) at 4 ml/s speed. Along with the dataset, we provide some utility files. dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition. dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models. dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.
description: {"references": ["Bennink E, Oosterbroek J, Kudo K, Viergever MA, Velthuis BK, de Jong HWAM. Fast nonlinear regression method for CT brain perfusion analysis. Journal of Medical Imaging 2016. https://doi.org/10.1117/1.jmi.3.2.026003.", "Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 2010. https://doi.org/10.1109/TMI.2009.2035616.", "Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the IEEE International Conference on Computer Vision, 1998. https://doi.org/10.1109/iccv.1998.710815."]}
DOI: 10.15132/10000162
title: QMB Morning Dataset

description: The proliferation of wearable cameras has accelerated and facilitated a surge in research on analysis of egocentric videos. This relatively new field has relatively few research datasets. A significant share of publicly available egocentric datasets is purposely acquired for activity recognition, video summarization, object detection, and behavioural understanding. Here we share our dataset that is focused on personal localization and mapping.

We collected our dataset on the university campus, documenting a user’s typical morning. The recording is always initiated at the building entrance, at which point the user enters and triggers the session. The dataset was collected indoors before working hours to ensure people were not mistakenly captured, avoiding potential privacy challenges. To further guard ourselves, we recorded the dataset exclusively in an indoor environment to limit strangers’ appearance in the field of view. The five sessions cover a one-month period. During recording, the user’s operation was in line with a loosely worded script to ensure that multiple visits were made to a range of locations. In total, we captured nine distinct stations: Entrance, 3d-lab, Kitchen 1, Kitchen 2, Cafe-area, Lab, Printer 1, Printer 2 and Office.
DOI: 10.15468/dl.oex9hk
title: Occurrence Download

description: A dataset containing 190 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Equisetales",
 "TaxonKey is Selaginellales",
 "TaxonKey is Lycopodiales",
 "TaxonKey is Isoetales",
 "TaxonKey is Ophioglossales"
 ]
 },
 "Country is Benin"
 ]
}

 The dataset includes 190 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mynf7j
title: Occurrence Download

description: A dataset containing 1971376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lepidoptera)",
 "Country is Austria"
 ]
}

 The dataset includes 1971376 records from 139 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025538-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p2z5r5
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cinara cupressi (Buckton, 1881)"
 ]
}

 The dataset includes 194 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007370-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rvsjvc
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is VFD-H: Heidenrod: Weide am Ortsrand"
 ]
}

 The dataset includes 83 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-160822134323880/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xz5cv6
title: Occurrence Download

description: A dataset listing the 2317 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-237.24646 41.2123,-235.60528 41.2123,-235.60528 42.01514,-237.24646 42.01514,-237.24646 41.2123))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 2317 records were derived from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056442-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ephzu3
title: Occurrence Download

description: A dataset containing 1537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Glyptemys insculpta (Le Conte, 1830)"
 ]
}

 The dataset includes 1537 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066026-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vj5xyh
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meiofauna densities from in situ feeding experiments in the Nazare Canyon conducted with sediments from ROV push-core JC10-95-PUC02"
 ]
}

 The dataset includes 21 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2c79ma
title: Occurrence Download

description: A dataset containing 15143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crotalus atrox Baird & Girard, 1853"
 ]
}

 The dataset includes 15143 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066039-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pqkgas
title: Occurrence Download

description: A dataset containing 4788 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corylus heterophylla Fisch. ex Trautv.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4788 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037216-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8tiplf
title: Occurrence Download

description: A dataset containing 2826 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galium sterneri Ehrend."
 ]
}

 The dataset includes 2826 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fpbmtc
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cirsium ×glabrifolium (C.Winkl.) O.Fedtsch. & B.Fedtsch."
 ]
}

 The dataset includes 10 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0138265-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ax0mke
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Larix ×polonica Racib.",
 "DecimalLatitude is greater than or equal to -90",
 "DecimalLongitude is greater than or equal to -180"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023270-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9oj6gx
title: Occurrence Download

description: A dataset containing 607514 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Plant"
 ]
}

 The dataset includes 607514 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.