cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ferz6w
title: Occurrence Download

description: A dataset containing 19742 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Syncerus caffer"
 ]
}

 The dataset includes 19742 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004740-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13843493
title: Bellabeat Activity & Cycle Dataset
DOI: 10.15468/dl.zqnz3j
title: Occurrence Download

description: A dataset containing 36695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Papilionidae",
 "GadmGid is one of (IND)"
 ]
}

 The dataset includes 36695 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004741-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ew6cg
title: Occurrence Download

description: A dataset containing 10490 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tragelaphus strepsiceros"
 ]
}

 The dataset includes 10490 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004742-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zrgjfm
title: Occurrence Download

description: A dataset containing 1673282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 1673282 records from 442 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024669-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.np6q25
title: Occurrence Download

description: A dataset containing 296 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyprinus carpio",
 "HasCoordinate is true",
 "Country is Poland"
 ]
}

 The dataset includes 296 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024693-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u296f4
title: Occurrence Download

description: A dataset containing 1846 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Herpailurus yagouaroundi (É.Geoffroy Saint-Hilaire, 1803), Puma yagouaroundi (É.Geoffroy Saint-Hilaire, 1803), Felis yagouaroundi É.Geoffroy Saint-Hilaire, 1803)",
 "Year 2010-2024"
 ]
}

 The dataset includes 1846 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001232-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15421706
title: Event-based Lunar OPtical flow Egomotion estimation (ELOPE) dataset

description: The "ELOPE" dataset is the official dataset of ESA's Kelvins ELOPE challenge, created in collaboration with:



European Space Agency (ESA)

Delft University of Technology (TU Delft)

University of Adelaide (UoA)


The Purpose of ELOPE is to investigate to what degree it is possible to estimate the velocity of a lunar lander during descent from an optical event stream. Additionally, a rangemeter measurement is provided as well as data from a Inertial Measurment Unit (IMU).

Event cameras (sometimes called neuromorphic vision sensors) allow a novel kind of vision, where pixels correspond to intensity changes and are able to fire indepedently from each other (asychronous). The ELOPE dataset contains synthetic event streams that were generated using high resolution images rendered by the Planet and Asteroid Natural Scene Generation Utility (PANGU) developed by the University of Dundee's Space Technology Centre and post-processed with the v2e camera emulator developed by ETH Zurich. 

The dataset consists of 93 landing sequences, each representing a descend towards a region of the Moon at different times, resulting in challenging lighting conditions and a variety of surface features. The lander is moving from a high gate at about 2-3km to a low gate at about 150m (no touch down) and might perform any number of maneuvers in between.

Our sequences are split into two categories: train and test. Each sequence is available as a compressed numpy-file (.npz-file). After loading such a file, for example

sequence = np.load('0000.npz')

you can access the sequence like a dictionary to obtain the following information:



events: A sequence of (x,y,p,t) entries, encoding events recorded by a simulated event-camera



x: event coordinate [0, 199]

y: event coordinate [0, 199]

p: polarity [True, False]

t: timestamp [µs]



timestamps: A sequence of timestamps related to the landers motion [s]

traj: The trajectory of lander for each of the timestamps as a state-vector (x,y,z,vx,vy,vz,phi,theta,psi,p,q,r)



x,y,z: position in global frame (the attitude z is given negative by convention) [m]

vx, vy, vz: velocity vector [m/s]

phi, theta, psi: roll, pitch, jaw Euler angles [rad]

p, q, r: corresponding angular velocity [rad/s]



rangemeter: A sequence (t, d) that represents the distance from the lander to the pixel [100,100] at short intervals 



t: timestamps [s]

d: distance [m]




The sequences in train have full information about the flight path of the lander and can be used as examples to gain insights into the general problem. The sequences in test have information about position (x,y,z) and velocity (vx,vy,vz) masked out using nan.

More details about the competition setup and solution evaluation are on the Kelvins competition platform.
DOI: 10.5281/zenodo.15421707
title: Event-based Lunar OPtical flow Egomotion estimation (ELOPE) dataset

description: The "ELOPE" dataset is the official dataset of ESA's Kelvins ELOPE challenge, created in collaboration with:



European Space Agency (ESA)

Delft University of Technology (TU Delft)

University of Adelaide (UoA)


The Purpose of ELOPE is to investigate to what degree it is possible to estimate the velocity of a lunar lander during descent from an optical event stream. Additionally, a rangemeter measurement is provided as well as data from a Inertial Measurment Unit (IMU).

Event cameras (sometimes called neuromorphic vision sensors) allow a novel kind of vision, where pixels correspond to intensity changes and are able to fire indepedently from each other (asychronous). The ELOPE dataset contains synthetic event streams that were generated using high resolution images rendered by the Planet and Asteroid Natural Scene Generation Utility (PANGU) developed by the University of Dundee's Space Technology Centre and post-processed with the v2e camera emulator developed by ETH Zurich. 

The dataset consists of 93 landing sequences, each representing a descend towards a region of the Moon at different times, resulting in challenging lighting conditions and a variety of surface features. The lander is moving from a high gate at about 2-3km to a low gate at about 150m (no touch down) and might perform any number of maneuvers in between.

Our sequences are split into two categories: train and test. Each sequence is available as a compressed numpy-file (.npz-file). After loading such a file, for example

sequence = np.load('0000.npz')

you can access the sequence like a dictionary to obtain the following information:



events: A sequence of (x,y,p,t) entries, encoding events recorded by a simulated event-camera



x: event coordinate [0, 199]

y: event coordinate [0, 199]

p: polarity [True, False]

t: timestamp [µs]



timestamps: A sequence of timestamps related to the landers motion [s]

traj: The trajectory of lander for each of the timestamps as a state-vector (x,y,z,vx,vy,vz,phi,theta,psi,p,q,r)



x,y,z: position in global frame (the attitude z is given negative by convention) [m]

vx, vy, vz: velocity vector [m/s]

phi, theta, psi: roll, pitch, jaw Euler angles [rad]

p, q, r: corresponding angular velocity [rad/s]



rangemeter: A sequence (t, d) that represents the distance from the lander to the pixel [100,100] at short intervals 



t: timestamps [s]

d: distance [m]




The sequences in train have full information about the flight path of the lander and can be used as examples to gain insights into the general problem. The sequences in test have information about position (x,y,z) and velocity (vx,vy,vz) masked out using nan.

More details about the competition setup and solution evaluation are on the Kelvins competition platform.
DOI: 10.15468/dl.bxxqg8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Barbodes lanaoensis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049385-250811113504898/datasets/export for details.
DOI: 10.6084/m9.figshare.27959172.v5
title: Mapping crop type in Northeast China during 2017-2024 (Including code for classification)

description: Northeast China is one of the most major grain production regions in China and has an overwhelming influence on regional and national food security. Crop type mapping is important for crop managements to maximize crop yield. We produced annual 10-m major crops (rice, maize and soybean) maps in Northeast China from 2017 to 2024, using a practical and transferable crop mapping approach by constructing crop-specific OIF knowledge graph (OIFKG). The overall accuracies higher than 90% (Kappa coefficient greater than 0.90), and the producer’s accuracies for rice, maize, and soybean are generally greater than 0.92 based on ground truth data. The crop maps have close agreement with the statistical data at the municipal level. This study provides a highly reliable long-term crop maps dataset, which can be helpful for regional agricultural production management.
DOI: 10.15468/dl.nxnfpx
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Terniopsis yongtaiensis"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049386-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/b6900166-ded6-4f7a-8734-484b6f77b2f1
title: Comparative transcriptomics of a complex of four European pine species

description: Data from whole transcriptome sequencing of the four European pine species - Pinus sylvestris (Scots pine), P. mugo (Dwarf mountain pine), P. uliginosa (Mountain pine) and P. uncinata (Peat-bog pine). Those data include
  1) transcriptome sequence of a reference Scots pine sample generated using Illumina paired-end sequencing technology and
  2) information about single nucleotide polymorphisms (SNPs) in 17 samples collected across the European range, compared to the reference Scots pine sample.

These genomic resources were produced to advance the study of the genetic basis of adaptive variation and speciation in pines. 
description: 1. Lineage of the reference transcriptome sequence of Scots pine samples from Glen Tanar (Scotland). Total Ribonucleic acid (RNA) was extracted from needles using Spectrum (TM) Plant Total RNA Kit (Sigma). A template complementary DNA (cDNA) library was prepared using ®TruSeq RNA Sample Preparation Kit (Illumina). The cDNA library was sequenced using Illumina HiSeq 2000 platform at Edinburgh Genomics, the University of Edinburgh, Scotland (Illumina, San Diego, California). 100 base, paired-end reads were generated and assembled de novo into contigs using Trinity (version r2012-06-08). The reference transcript sequence contains 40798 contigs in total. 

2. The single nucleotide polymorphism (SNP) data file was prepared by aligning sixteen samples to the reference, a sample set that included P. sylvestris (four samples), P. mugo (five samples), P. uncinata (four samples) and P. uliginosa (three samples). Samples collected between 2006 and 2010.
DOI: 10.5285/271d398c-29de-4ff9-ba68-1b8e0621c6ee
title: Countryside Survey 1984 estimates of linear feature lengths in Great Britain

description: This dataset consists of stock (length) data for linear features across Great Britain in 1984. The data are national estimates generated by analysing the sample data from 384 1km squares surveyed for the Countryside Survey long term monitoring project, then scaling up to a national level. The data are presented as mean lengths for 7 different feature categories within 45 different land class types, based on the ITE Land Classification. The Countryside Survey is a unique study or 'audit' of the natural resources of the UK's countryside, carried out by the Centre for Ecology & Hydrology. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 with repeated visits to the majority of squares. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. In addition to linear features, habitat areas, species plot, soil plot, freshwater habitat and satellite map data are also produced by Countryside Survey.
description: The data were recorded onto paper field sheets in the summer of 1984 by surveyors. Data were recorded from 384 1km squares sited across Britain, selected by random stratified sample. The boundary lines were digitized in the 1990s. The digitized data were used to generate national estimates of the stock of linear features for Great Britain using the standard Countryside Survey methodology (refer to the online documentation). Estimates are presented by ITE Land Class (2007 version), as mean length per Land Class.
DOI: 10.5285/8a41b2a2-01d7-409e-adf5-fba3f3770f29
title: Plant and soil responses to simulated summer drought in 2013 on Colt Park grassland restoration experiment

description: Data comprise measurements of plant biomass and community composition, soil microbial community composition, greenhouse gas emissions and soil carbon and nitrogen pools from a drought experiment superimposed on a the long-term Colt Park grassland restoration experiment in northern England. Rainfall was manipulated using rain-out shelters on experimental grassland plots where fertiliser application and seed addition have been managed to enhance plant species diversity. The scientific purpose was to test the hypothesis that management aimed at biodiversity restoration increases the resistance and recovery of carbon cycling to short-term summer drought.
description: Location: Colt Park meadows, Ingleborough National Nature Reserve (latitude 54°12’N, longitude 2°21’W). The two grassland restoration treatments used in this experiment were fertiliser application and seed addition. The drought treatment was superimposed onto these two treatments. All experimental plots were cut for hay on 16 July in 2013. After the hay cut, all experimental plots were grazed by cattle and sheep. Since 1990, half of plots received continued fertiliser application and half receive no fertiliser, with the exception of 2009 and 2010 when fertiliser was not applied. Inorganic fertiliser (NPK 20:10:10; 25kg N ha-1) was applied by hand in spring (21 May in 2013). Seed addition began in 1990 with seed of 19 species being locally collected and commercially bought. The drought treatment had three levels, a) ambient, consisting of no rain-out shelter; b) control shelter, consisting of a rain shelter with holes; c) drought, consisting of a rain-out shelter. Rain-out shelters were in place from 5 June to 10 July 2013. They were constructed of transparent corrugated PVC, 0.8mm thick (Corolux, UK) and were 90cm x 105cm with a height of 38cm-63cm and a slope of 16 degrees.
DOI: 10.15468/dl.56jz4y
title: Occurrence Download

description: A dataset containing 963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Mammillaria magnimamma",
 "Year 2020-2025"
 ]
}

 The dataset includes 963 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031268-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/18b7c387-037d-4949-98bc-e8db5ef4264c
title: UK Environmental Change Network (ECN) precipitation chemistry data: 1992-2015

description: Precipitation chemistry data from the UK Environmental Change Network (ECN) terrestrial sites. Variables measured include pH, conductivity, alkalinity, aluminium, calcium, chloride, ammonium, nitrate nitrogen, phosphate phosphorous, potassium, sulphate sulphur, sodium, total nitrogen and total dissolved phosphorous. These data are collected by a bulk collector at all of ECN's terrestrial sites using a standard protocol. They represent continuous weekly records from 1992 to 2015. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage.
description: Data are collected by a bulk collector at all of ECN's terrestrial sites using a standard protocol. Prior to deposit of the data in the EIDC, data are managed by the ECN Data Centre at CEH Lancaster according to defined protocols. 

Verification steps include numeric range checks (i.e. checking if a value falls within a specified range), categorical checks (e.g. checking that a species code appears on the standard code list), formatting (i.e. that the dataset conforms to the specified data format) and logical integrity checks (i.e. checking the data make sense, e.g. that the dates in one dataset match those in a related dataset).

Appropriate range settings for ECN variables have been selected following discussion with specialists in each field. Where data fall outside these ranges, a cautious approach has been adopted towards discarding data on the principle that apparent errors may be valid outliers. Such values are discarded only if there is a clear explanation (e.g. an instrumentation error) and corrections are made where possible. If the reason is unclear, the values are stored, but are qualified using pre-defined quality codes or free-text descriptions. Data providers also use these codes or free text to describe factors affecting sampling outside their control, instrument damage or site management effects.
DOI: 10.5285/7b2071d6-da02-4f48-a87e-ccab19a737bd
title: Digital Elevation Model of an experimental river generated in a flume under variable discharge conditions

description: This dataset contains the elevation data of a 10m long, 20cm wide experimental river. This river was generated in an experimental flume (Total Environment Simulator) of the University of Hull. This river was formed spontaneously by imposing water and sediment flux at the upstream boundary of a flat sand bed. This model doesn’t attempt to reproduce any natural river, it’s a generic model design to explore the influence of discharge variation on the river evolution. We regularly measure the topography of the experimental area to follow the evolution of the river width, depth, slope, and conveyance capacity. The elevation is measured in metres.  The associated table contains the experimental characteristics: time (seconds), water discharge (L/min), and morphological characteristics extracted from the DEM. This is the raw data, so the width is in pixels, the depth is in metres and the conveyance capacity is in metre pixel.
description: The data have been generated using a terrestrial laser scanner (Faro Focus X330). The laser scanner is in a fixed position above the experimental area. Seven targets (coordinates known) are erected in the area. Each scan is therefore reoriented using the location of the target, using the Faro software (Scene). We only accept scans with a re-orientation error lower than 0.5 mm. If the error is higher, we redo the scan.

The duration of a scan is about 10 minutes and the time between each scan depends on the experimental conditions (the exact duration between each scan can be found in the experimental data file).

Once the scan has been reoriented, we pre-process it using CloudCompare to select only the experimental area (remove the wall and instrument), we then convert this into a tiff file to analyse with python. 

The scanner can’t see the surface under water, consequently we have to ensure that the experimental area is dry before we start scanning. If the surface is not dry the scan returns NaN values.
DOI: 10.5285/299f89cd-7bbe-4428-9552-c51981c5f883
title: Heterogeneity and environmental stress resistance of wild yeast isolates

description: This dataset contains analytical results for wild yeast isolates from soil samples at and near a disused metal smelting works in the north-east of the UK. The main contaminant from the smelting works was lead (Pb), but Cd was also elevated. The soil near the smelting works showed above-background levels of Pb. Yeasts of similar colony morphology on laboratory agar were isolated and proved to be mostly isolates of Saitozyma podzolica: 56 independent clonal isolates of this yeast were confirmed by ITS sequencing and RAPD-PCR. All of the S. podzolica isolates were tested for Pb and Cd resistance (IC50) and heteroresistance. Heteroresistance indicated variation in metal resistance between individual cells of clonal populations. Some isolates were also tested with Zn. The genomes of the isolates were sequenced and sequence data submitted to the NCBI/EBI.
description: Soil-core samples were collected during July 2014 near the disused St. Anthony’s metal smelting works, at either contaminated (16.03 g kg-1 Pb, 10.87 mg Kg-1 Cd) or nearby less-contaminated (4.38 g kg-1 Pb, 1.49 mg Kg-1 Cd) areas. Coordinates and a description of the site are available at http://www.twsitelines.info/SMR/4192. Soil-core samples were diluted in the laboratory and spread to agar media for yeast isolation. Single colonies were subcultured and yeasts identified with ITS sequencing and RAPD PCR. 56 clonal isolates of Saitozyma podzolica were assayed for lead (Pb) and cadmium (Cd) resistance (IC50) and heteroresistance. Some isolates were also tested with zinc (Zn) Genomic DNA preparations submitted to NBAF-Liverpool for whole-genome sequencing. The sequence data were deposited with EBI/NCBI.