cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/n795-24a4
title: Measurement of Sulphate_corrected at Ulborg

description: Measurement of Sulphate_corrected at Ulborg
DOI: 10.48597/n9wj-cfza
title: Measurement of Sulphate_corrected at Ispra

description: Measurement of Sulphate_corrected at Ispra
DOI: 10.48597/ncre-nq69
title: Measurement of Sulphate_corrected at Rucava

description: Measurement of Sulphate_corrected at Rucava
DOI: 10.48597/p68q-c3g6
title: Measurement of Sulphate_corrected at Malin Head

description: Measurement of Sulphate_corrected at Malin Head
DOI: 10.48597/p8ar-kyts
title: Measurement of Sulphate_corrected at Verneuil

description: Measurement of Sulphate_corrected at Verneuil
DOI: 10.48597/phda-3xts
title: Measurement of Sulphate_corrected at Svratouch

description: Measurement of Sulphate_corrected at Svratouch
DOI: 10.48597/qbeg-5wgg
title: Measurement of Sulphate_corrected at Lahemaa

description: Measurement of Sulphate_corrected at Lahemaa
DOI: 10.48597/rd48-jhgv
title: Measurement of Sulphate_corrected at Niembro

description: Measurement of Sulphate_corrected at Niembro
DOI: 10.48597/swtd-rgy8
title: Measurement of Sulphate_corrected at Valentia Observatory

description: Measurement of Sulphate_corrected at Valentia Observatory
DOI: 10.48597/t2e7-rcpz
title: Measurement of Sulphate_corrected at Leova II

description: Measurement of Sulphate_corrected at Leova II
DOI: 10.48597/t8fm-kb53
title: Measurement of Sulphate_corrected at Le Casset

description: Measurement of Sulphate_corrected at Le Casset
DOI: 10.48597/t9v4-fb7y
title: Measurement of Sulphate_corrected at Anholt

description: Measurement of Sulphate_corrected at Anholt
DOI: 10.48597/tchg-ccea
title: Measurement of Sulphate_corrected at Penausende

description: Measurement of Sulphate_corrected at Penausende
DOI: 10.48597/td3e-n84b
title: Measurement of Sulphate_corrected at Donon

description: Measurement of Sulphate_corrected at Donon
DOI: 10.48597/ugzw-drt2
title: Measurement of Sulphate_corrected at Tange

description: Measurement of Sulphate_corrected at Tange
DOI: 10.48597/ux85-6wnd
title: Measurement of Sulphate_corrected at Preila

description: Measurement of Sulphate_corrected at Preila
DOI: 10.48597/v6dy-grxw
title: Measurement of Sulphate_corrected at High Muffles

description: Measurement of Sulphate_corrected at High Muffles
DOI: 10.48597/vb5m-4kbk
title: Measurement of Sulphate_corrected at Valentia Observatory

description: Measurement of Sulphate_corrected at Valentia Observatory
DOI: 10.48597/vftx-erru
title: Measurement of Sulphate_corrected at Malin Head

description: Measurement of Sulphate_corrected at Malin Head
DOI: 10.48597/vjs2-8rdu
title: Measurement of Sulphate_corrected at La Tardière

description: Measurement of Sulphate_corrected at La Tardière
DOI: 10.5281/zenodo.10780380
title: Data for "On the Formation of Trapped Electron Radiation Belts at Ganymede" by Liuzzo et al.

description: Data for "On the Formation of Trapped Electron Radiation Belts at Ganymede" by Liuzzo et al.
description: Supported by NASA Solar System Workings grant 80NSSC21K0823 and the France Berkeley Fund grant 15_2023.
DOI: 10.5281/zenodo.10557206
title: Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature

description: Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.
DOI: 10.5281/zenodo.8352240
title: Range expansion is slower and less predictable across a spatial gradient in temperature

description: Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent studies suggest that how evolution influences the predictability of range expansion speed depends on the balance of genetic drift and selection at the range front, with factors increasing selection proposed to produce greater consistency, and hence increased predictability, in range expansion speed. Here, we test whether selection from environmental gradients across space produces more predictable range expansion speeds, using the experimental evolution of replicate, duckweed populations colonizing landscapes with and without a temperature gradient. While range expansion across a temperature gradient was slower, with range-front populations displaying higher population densities and genetic signatures consistent with greater selection, we found that range expansion speed was more variable and less predictable across replicate populations. Our results therefore challenge current theory, highlighting that selection and spatial priority effects could together govern the predictability of range expansion speeds.
DOI: 10.5281/zenodo.10779255
title: Machine Learning-based Human Life Detection Behind Walls Exploiting a UWB Radar Sensor

description: The "Signs of life detection behind obstacles using a UWB radar sensor" dataset was created by the University of West Attica, by collecting data from 9 people using a UWB sensor, which was the X4M200 UWB radar sensor by Novelda, with the existence of a wall between the subject and the radar for the purposes of RESCUER project (https://rescuerproject.eu/ - a Horizon 2020 Research & Innovation Programme under Grant Agreement No.101021836).

Two datasets were collected. The first includes data recorded from standing humans positioned behind a wall of 30 cm thickness, and the second incorporates data gathered from lying down humans positioned behind a wall 20 cm thick and distant from the radar between 1 and 4 m. In both datasets, the BioHarness Zephyr 3 belt that estimates the breathing rate of the victims was exploited to provide the ground truth measurements of the breathing rate in each data collection session. Each session lasted about 90 seconds, and the belt provided the breathing rate every second.

To obtain reliable measurements, we averaged the breathing rate after the first 60 seconds to acquire a single value for all samples of the same data collection session. Next, the human was moved 0.5 m away from its current position to gather data from the next session. This procedure was followed until the human reached a 4 m distance from the radar, allowing the acquisition of data from a total of 7 sessions (from 1 to 4 m).

Additionally, we collected a number of data from the same places without any human presence to give the opportunity to train Machine Learning (ML) algorithms to detect human presence behind the wall. In particular, the number of sessions without human presence is 14 for the dataset including standing persons, and 10 for the dataset incorporating lying down persons.

To facilitate the ML training using more samples, apart from the raw data, the dataset also contains overlapped data. We considered samples of 20-sec duration, exploiting an overlap of 15 sec over this 20-sec window. The labels for distance and breathing rate for the overlapped data are in the corresponding files. For the non-overlapped data, the file name contains the two labels, e.g. the file "clean 1_10.85.csv" denotes a distance of 1 m between the human and the radar and a breathing rate of the human equal to 10.85 breaths/min. Finally, the sampling rate of the radar was set to 17 samples per second and a distance step of about 0.05144 m was also considered. As a consequence the first row of each file denotes the different distances (over the range of 0.48 m to 6 m).
DOI: 10.5281/zenodo.10779256
title: Machine Learning-based Human Life Detection Behind Walls Exploiting a UWB Radar Sensor

description: The "Signs of life detection behind obstacles using a UWB radar sensor" dataset was created by the University of West Attica, by collecting data from 9 people using a UWB sensor, which was the X4M200 UWB radar sensor by Novelda, with the existence of a wall between the subject and the radar for the purposes of RESCUER project (https://rescuerproject.eu/ - a Horizon 2020 Research & Innovation Programme under Grant Agreement No.101021836).

Two datasets were collected. The first includes data recorded from standing humans positioned behind a wall of 30 cm thickness, and the second incorporates data gathered from lying down humans positioned behind a wall 20 cm thick and distant from the radar between 1 and 4 m. In both datasets, the BioHarness Zephyr 3 belt that estimates the breathing rate of the victims was exploited to provide the ground truth measurements of the breathing rate in each data collection session. Each session lasted about 90 seconds, and the belt provided the breathing rate every second.

To obtain reliable measurements, we averaged the breathing rate after the first 60 seconds to acquire a single value for all samples of the same data collection session. Next, the human was moved 0.5 m away from its current position to gather data from the next session. This procedure was followed until the human reached a 4 m distance from the radar, allowing the acquisition of data from a total of 7 sessions (from 1 to 4 m).

Additionally, we collected a number of data from the same places without any human presence to give the opportunity to train Machine Learning (ML) algorithms to detect human presence behind the wall. In particular, the number of sessions without human presence is 14 for the dataset including standing persons, and 10 for the dataset incorporating lying down persons.

To facilitate the ML training using more samples, apart from the raw data, the dataset also contains overlapped data. We considered samples of 20-sec duration, exploiting an overlap of 15 sec over this 20-sec window. The labels for distance and breathing rate for the overlapped data are in the corresponding files. For the non-overlapped data, the file name contains the two labels, e.g. the file "clean 1_10.85.csv" denotes a distance of 1 m between the human and the radar and a breathing rate of the human equal to 10.85 breaths/min. Finally, the sampling rate of the radar was set to 17 samples per second and a distance step of about 0.05144 m was also considered. As a consequence the first row of each file denotes the different distances (over the range of 0.48 m to 6 m).
DOI: 10.5281/zenodo.7679165
title: Signs of life detection behind obstacles using an UWB radar sensor

description: The "Signs of life detection behind obstacles using an UWB radar sensor" dataset was created by University of West Attica, by collecting data from 9 people using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda, with and without the existence of a wall between the subject and the radar for the purposes of RESCUER project (https://rescuerproject.eu/ - a Horizon 2020 Research & Innovation Programme under Grant Agreement No.101021836). In all cases, the subject was lying down in the same stance for about 1 min and the radar was placed in four different positions: a) 0.2 m from the ground, b) 0.5 m from the ground, c) 1 m from the ground, d) 1 m from the ground and with a 45 degree angle. For comparison purposes, we also include in the dataset the case where no human is present within the radar's detection area. The subjects were placed in a distance between 0.5 and 5 metres from the radar and different stances were considered, while data were collected both for the amplitude (designated as abs) and the phase of the signal (designated as angle). This information is designated in each filename. For example, the filename "2 facing radar abs.csv", indicates that the subject is placed in 2 metres away from the radar, he/she is facing the radar and the file contains the signal's amplitude. The sampling rate of the radar was set to 17 samples per second and a distance step of about 0.05144 m was also considered.
DOI: 10.5281/zenodo.8352234
title: Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature

description: Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.
DOI: 10.5281/zenodo.8352235
title: Range expansion is slower and less predictable across a spatial gradient in temperature

description: Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent studies suggest that how evolution influences the predictability of range expansion speed depends on the balance of genetic drift and selection at the range front, with factors increasing selection proposed to produce greater consistency, and hence increased predictability, in range expansion speed. Here, we test whether selection from environmental gradients across space produces more predictable range expansion speeds, using the experimental evolution of replicate, duckweed populations colonizing landscapes with and without a temperature gradient. While range expansion across a temperature gradient was slower, with range-front populations displaying higher population densities and genetic signatures consistent with greater selection, we found that range expansion speed was more variable and less predictable across replicate populations. Our results therefore challenge current theory, highlighting that selection and spatial priority effects could together govern the predictability of range expansion speeds.
DOI: 10.6073/pasta/8d28fa9c31095c4c15836cbd3467f0d1
title: Extreme Drought in Grassland Ecosystems (EDGE) Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico

description: Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV297, "Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
DOI: 10.6084/m9.figshare.25338703
title: RNA targeting and cleavage by the type III-Dv CRISPR effector complex

description: The model structures of the <i>Synechocystis</i> type III-Dv complex, an apparent evolutionary intermediate from multi-protein to single-protein type III effectors.
DOI: 10.48597/wgrm-42mc
title: Measurement of Sulphate_corrected at Montfranc

description: Measurement of Sulphate_corrected at Montfranc
DOI: 10.48597/wnd3-xtv9
title: Measurement of Sulphate_corrected at Rigi

description: Measurement of Sulphate_corrected at Rigi
DOI: 10.48597/wrsd-yn8t
title: Measurement of Sulphate_corrected at San Pablo de los Montes

description: Measurement of Sulphate_corrected at San Pablo de los Montes
DOI: 10.48597/xaac-3bc6
title: Measurement of Sulphate_corrected at Reykjavik

description: Measurement of Sulphate_corrected at Reykjavik
DOI: 10.48597/z7du-ddzn
title: Measurement of Sulphate_corrected at Revin

description: Measurement of Sulphate_corrected at Revin
DOI: 10.48597/2awv-7xuv
title: Measurement of Benzo_ghi_perylene at Auchencorth Moss

description: Measurement of Benzo_ghi_perylene at Auchencorth Moss
DOI: 10.48597/zqww-4tb7
title: Measurement of Sulphate_corrected at Revin

description: Measurement of Sulphate_corrected at Revin
DOI: 10.48597/2kzf-n3sc
title: Measurement of Benzo_b_fluoranthene at Harwell

description: Measurement of Benzo_b_fluoranthene at Harwell
DOI: 10.48597/2nmd-ffch
title: Measurement of Benzo_e_pyrene at Harwell

description: Measurement of Benzo_e_pyrene at Harwell
DOI: 10.48597/3afu-9dyw
title: Measurement of Benzo_b_naphtho_21_d_thiophene at Harwell

description: Measurement of Benzo_b_naphtho_21_d_thiophene at Harwell
DOI: 10.48597/3bje-edxy
title: Measurement of Benzo_a_pyrene at Auchencorth Moss

description: Measurement of Benzo_a_pyrene at Auchencorth Moss
DOI: 10.48597/3mne-f6q3
title: Measurement of Benzo_j_fluoranthene at Harwell

description: Measurement of Benzo_j_fluoranthene at Harwell
DOI: 10.48597/3nyz-ysc8
title: Measurement of Lead at Harwell

description: Measurement of Lead at Harwell
DOI: 10.48597/3zkv-xtv4
title: Measurement of Carbon_monoxide at Lahemaa

description: Measurement of Carbon_monoxide at Lahemaa
DOI: 10.48597/48aa-pxcd
title: Measurement of Benzo_k_fluoranthene at Harwell

description: Measurement of Benzo_k_fluoranthene at Harwell
DOI: 10.48597/4m66-77vu
title: Measurement of Chrysene at Harwell

description: Measurement of Chrysene at Harwell
DOI: 10.48597/4vqz-hfwt
title: Measurement of Benzo_b_fluoranthene at Auchencorth Moss

description: Measurement of Benzo_b_fluoranthene at Auchencorth Moss
DOI: 10.48597/5kg6-22sc
title: Measurement of Chromium at Harwell

description: Measurement of Chromium at Harwell
DOI: 10.48597/5pg9-58rk
title: Measurement of Manganese at Harwell

description: Measurement of Manganese at Harwell
DOI: 10.48597/5s5k-rzyn
title: Measurement of Benzo_bj_fluoranthenes at Harwell

description: Measurement of Benzo_bj_fluoranthenes at Harwell
DOI: 10.48597/5t2y-z8yr
title: Measurement of Coronene at Auchencorth Moss

description: Measurement of Coronene at Auchencorth Moss
DOI: 10.48597/5xpk-264k
title: Measurement of Arsenic at Harwell

description: Measurement of Arsenic at Harwell
DOI: 10.48597/5yrh-7una
title: Measurement of Iron_57 at Harwell

description: Measurement of Iron_57 at Harwell
DOI: 10.48597/62j6-j3vh
title: Measurement of Dibenzo_ac_anthracene at Auchencorth Moss

description: Measurement of Dibenzo_ac_anthracene at Auchencorth Moss
DOI: 10.48597/6mxt-7j2u
title: Measurement of Retene at Harwell

description: Measurement of Retene at Harwell
DOI: 10.48597/7ey9-4u8n
title: Measurement of Benzo_a_pyrene at Harwell

description: Measurement of Benzo_a_pyrene at Harwell
DOI: 10.48597/7ryj-p8zz
title: Measurement of Nitrogen_dioxide at Yarner Wood

description: Measurement of Nitrogen_dioxide at Yarner Wood
DOI: 10.48597/7s7a-yfcv
title: Measurement of Nitrogen_monoxide at High Muffles

description: Measurement of Nitrogen_monoxide at High Muffles
DOI: 10.48597/8256-p46p
title: Measurement of Benzo_ghi_perylene at Harwell

description: Measurement of Benzo_ghi_perylene at Harwell
DOI: 10.48597/82rq-6jvg
title: Measurement of Benzo_k_fluoranthene at Harwell

description: Measurement of Benzo_k_fluoranthene at Harwell
DOI: 10.48597/8aua-qmjh
title: Measurement of Carbon_monoxide at Lahemaa

description: Measurement of Carbon_monoxide at Lahemaa
DOI: 10.48597/8c3q-9fb2
title: Measurement of Carbon_monoxide at Lahemaa

description: Measurement of Carbon_monoxide at Lahemaa
DOI: 10.48597/8pge-fn88
title: Measurement of Coronene at Harwell

description: Measurement of Coronene at Harwell
DOI: 10.48597/94wg-7xxf
title: Measurement of Pyrene at Harwell

description: Measurement of Pyrene at Harwell
DOI: 10.48597/9g4k-enbu
title: Measurement of Anthanthrene at Harwell

description: Measurement of Anthanthrene at Harwell
DOI: 10.48597/9gwr-shtk
title: Measurement of Benz_a_anthracene at Harwell

description: Measurement of Benz_a_anthracene at Harwell
DOI: 10.48597/aekm-eq26
title: Measurement of Benzo_k_fluoranthene at Auchencorth Moss

description: Measurement of Benzo_k_fluoranthene at Auchencorth Moss
DOI: 10.48597/appq-72gh
title: Measurement of Nitrogen_monoxide at Yarner Wood

description: Measurement of Nitrogen_monoxide at Yarner Wood
DOI: 10.48597/ay78-6nb4
title: Measurement of Dibenzo_ac_ah_anthracenes at Auchencorth Moss

description: Measurement of Dibenzo_ac_ah_anthracenes at Auchencorth Moss
DOI: 10.48597/az7b-q8k2
title: Measurement of Pm25_mass at Harwell

description: Measurement of Pm25_mass at Harwell
DOI: 10.48597/b3fb-mm22
title: Measurement of Zinc at Harwell

description: Measurement of Zinc at Harwell
DOI: 10.48597/b48r-4vcr
title: Measurement of Benz_a_anthracene at Auchencorth Moss

description: Measurement of Benz_a_anthracene at Auchencorth Moss
DOI: 10.48597/b783-rbxk
title: Measurement of Benzo_a_pyrene at Auchencorth Moss

description: Measurement of Benzo_a_pyrene at Auchencorth Moss
DOI: 10.48597/bfs3-7yes
title: Measurement of Dibenzo_ac_ah_anthracenes at Auchencorth Moss

description: Measurement of Dibenzo_ac_ah_anthracenes at Auchencorth Moss
DOI: 10.48597/bka8-6etv
title: Measurement of Carbon_monoxide at Lahemaa

description: Measurement of Carbon_monoxide at Lahemaa
DOI: 10.48597/bwup-n5au
title: Measurement of Coronene at Harwell

description: Measurement of Coronene at Harwell
DOI: 10.48597/bz36-sa7b
title: Measurement of Benzo_e_pyrene at Harwell

description: Measurement of Benzo_e_pyrene at Harwell
DOI: 10.48597/cm3u-fq43
title: Measurement of Benzo_c_phenanthrene at Auchencorth Moss

description: Measurement of Benzo_c_phenanthrene at Auchencorth Moss
DOI: 10.48597/csdq-qxfs
title: Measurement of Dibenzo_ae_pyrene at Harwell

description: Measurement of Dibenzo_ae_pyrene at Harwell
DOI: 10.48597/d23t-jvh9
title: Measurement of Zinc at Auchencorth Moss

description: Measurement of Zinc at Auchencorth Moss
DOI: 10.48597/d2ns-targ
title: Measurement of Cyclopenta_cd_pyrene at Harwell

description: Measurement of Cyclopenta_cd_pyrene at Harwell
DOI: 10.48597/d4fz-dfr8
title: Measurement of Benzo_b_naphtho_21_d_thiophene at Auchencorth Moss

description: Measurement of Benzo_b_naphtho_21_d_thiophene at Auchencorth Moss
DOI: 10.15468/dl.y3waep
title: Occurrence Download

description: A dataset containing 322 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anolis petersii"
 ]
}

 The dataset includes 322 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011013-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.