cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12879944.v1
title: Additional file 4 of Multi-genome identification and characterization of chlamydiae-specific type III secretion substrates: the Inc proteins

description: Additional file 4:Table S4: Putative Inc proteins from C. abortus. (XLS 34 KB)
DOI: 10.6084/m9.figshare.7946267.v1
title: gVCF_NA20822

description: 1000 Genomes gVCF mapped to hs37d5 for NA20822. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946219
title: gVCF_NA20821

description: 1000 Genomes gVCF mapped to hs37d5 for NA20821. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946234
title: gVCF_NA18539

description: 1000 Genomes gVCF mapped to hs37d5 for NA18539. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946219.v1
title: gVCF_NA20821

description: 1000 Genomes gVCF mapped to hs37d5 for NA20821. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946234.v1
title: gVCF_NA18539

description: 1000 Genomes gVCF mapped to hs37d5 for NA18539. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946189
title: Unprocessed raw 30Hz acceleration data stored as .gt3x

description: Unprocessed raw 30Hz acceleration data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. Gt3x files are produced by Actigraph. (CSV files containing this data are also provided with this dataset).
DOI: 10.6084/m9.figshare.7946192
title: How to edit a manuscript on GitHub with Manubot

description: The screen recording shows how to edit a manuscript via GitHub using Manubot. Manubot is the the manuscript bot for automated scholarly publishing. It is an open source software project for writing scholarly documents using GitHub. Learn more at https://manubot.org.This screen recording is used as a supporting data video for the manuscript describing Manubot at https://greenelab.github.io/meta-review/.This latest version of this Figshare item is available at https://doi.org/10.6084/m9.figshare.7946192 or shortened to https://doi.org/c4fz.
DOI: 10.6084/m9.figshare.7946186
title: Unprocessed raw 30Hz acceleration data stored as .csv

description: Unprocessed raw 30Hz acceleration data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946183
title: Activity count data identified as prosthesis wear or non-wear

description: Actigraph 60s epoch activity count data which has been passed through the prosthesis non-wear algorithm. Each frame has been identified as wear (1) or non-wear (0).
DOI: 10.6084/m9.figshare.7946180
title: 60s epoch activity count data with low frequency extension filter stored as .csv

description: 60s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946189.v1
title: Unprocessed raw 30Hz acceleration data stored as .gt3x

description: Unprocessed raw 30Hz acceleration data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. Gt3x files are produced by Actigraph. (CSV files containing this data are also provided with this dataset).
DOI: 10.6084/m9.figshare.7946186.v1
title: Unprocessed raw 30Hz acceleration data stored as .csv

description: Unprocessed raw 30Hz acceleration data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946183.v1
title: Activity count data identified as prosthesis wear or non-wear

description: Actigraph 60s epoch activity count data which has been passed through the prosthesis non-wear algorithm. Each frame has been identified as wear (1) or non-wear (0).
DOI: 10.6084/m9.figshare.7946174
title: 60s epoch activity count data stored as .csv

description: 60s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946177
title: Self-reported wear diaries

description: Self-reported sleep times, prosthesis wear times and sensor wear times.
DOI: 10.6084/m9.figshare.7946180.v1
title: 60s epoch activity count data with low frequency extension filter stored as .csv

description: 60s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946171
title: 1s epoch activity count data with low frequency extension filter stored as .csv

description: 1s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946174.v1
title: 60s epoch activity count data stored as .csv

description: 60s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946168
title: 1s epoch activity count data stored as .csv

description: 1s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946171.v1
title: 1s epoch activity count data with low frequency extension filter stored as .csv

description: 1s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946177.v1
title: Self-reported wear diaries

description: Self-reported sleep times, prosthesis wear times and sensor wear times.
DOI: 10.6084/m9.figshare.7946162
title: 1s epoch activity count data stored as .agd

description: 1s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946165
title: 60s epoch activity count data with low frequency extension filter stored as .agd

description: 60s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946156
title: 60s epoch activity count data stored as .agd

description: 60s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946168.v1
title: 1s epoch activity count data stored as .csv

description: 1s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist.
DOI: 10.6084/m9.figshare.7946159
title: 1s epoch activity count data with low frequency extension filter stored as .agd

description: 1s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946165.v1
title: 60s epoch activity count data with low frequency extension filter stored as .agd

description: 60s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946162.v1
title: 1s epoch activity count data stored as .agd

description: 1s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946159.v1
title: 1s epoch activity count data with low frequency extension filter stored as .agd

description: 1s epoch activity count data collected over a 7 day period. This data was downloaded using Actigraph's low frequency extension filter. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946138
title: gVCF_NA20819

description: 1000 Genomes gVCF mapped to hs37d5 for NA20819. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946141
title: gVCF_NA18538

description: 1000 Genomes gVCF mapped to hs37d5 for NA18538. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946156.v1
title: 60s epoch activity count data stored as .agd

description: 60s epoch activity count data collected over a 7 day period. For each participant, 2 files are provided, one for the sensor on each wrist. AGD files can be opened in Actilife software.
DOI: 10.6084/m9.figshare.7946135
title: AridSoils

description: The repository contains the data used for paper "Surface evaporation in arid regions – insights from lysimeter decadal record and global application of a surface evaporation capacitor (SEC) model"
DOI: 10.6084/m9.figshare.7946114
title: gVCF_NA20818

description: 1000 Genomes gVCF mapped to hs37d5 for NA20818. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946138.v1
title: gVCF_NA20819

description: 1000 Genomes gVCF mapped to hs37d5 for NA20819. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946105
title: Phenological metrics at 97 global flux sites

description: <b>1. fluxsite97_locinfo.csv:</b>Flux sites information, including site name, IGBP and latitude. <br><b>2. fluxsite97_GPPobs_Phenology.xlsx:</b>Phenological metrics extracted from flux sites GPP observations at 97 flux sites.<br><b>3. fluxsite97_Remote Sensing Products-Phenology.xlsx:</b>Phenological metrics of two remote sensing GPP products (GPP_mod, GPP_vpm) and two remote sensing EVI products (MOD13A1_EVI, MOD13Q1_EVI) at 97 flux sites.<br>
DOI: 10.6084/m9.figshare.7946114.v1
title: gVCF_NA20818

description: 1000 Genomes gVCF mapped to hs37d5 for NA20818. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946087
title: gVCF_NA18537

description: 1000 Genomes gVCF mapped to hs37d5 for NA18537. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946135.v2
title: AridSoils

description: The repository contains the data used for paper "Surface evaporation in arid regions – insights from lysimeter decadal record and global application of a surface evaporation capacitor (SEC) model"
DOI: 10.6084/m9.figshare.7946087.v1
title: gVCF_NA18537

description: 1000 Genomes gVCF mapped to hs37d5 for NA18537. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946105.v2
title: Phenological metrics at 97 global flux sites

description: <b>1. fluxsite97_locinfo.csv:</b>Flux sites information, including site name, IGBP and latitude. <br><b>2. fluxsite97_GPPobs_Phenology.xlsx:</b>Phenological metrics extracted from flux sites GPP observations at 97 flux sites.<br><b>3. fluxsite97_Remote Sensing Products-Phenology.xlsx:</b>Phenological metrics of two remote sensing GPP products (GPP_mod, GPP_vpm) and two remote sensing EVI products (MOD13A1_EVI, MOD13Q1_EVI) at 97 flux sites.<br>
DOI: 10.6084/m9.figshare.7946135.v1
title: AridSoils

description: The repository contains the data used for paper "Surface evaporation in arid regions"
DOI: 10.6084/m9.figshare.7946105.v1
title: Phenology metrics at global flux sites

description: <b>1. fluxsite97_locinfo.csv:</b>Flux sites information, including site name, IGBP and latitude. <br><b>2. 'fluxsite97_GPPobs_Phenology.xlsx':</b>Phenological metrics extracted from flux sites GPP observations.<br><b>3. 'fluxsite97_Remote Sensing Products-Phenology.xlsx':</b>Phenological metrics of two remote sensing GPP products (GPP_mod, GPP_vpm) and two remote sensing EVI products (MOD13A1_EVI, MOD13Q1_EVI).<br>
DOI: 10.6084/m9.figshare.7946060
title: gVCF_NA20815

description: 1000 Genomes gVCF mapped to hs37d5 for NA20815. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946060.v1
title: gVCF_NA20815

description: 1000 Genomes gVCF mapped to hs37d5 for NA20815. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946027
title: Supplementary tables. Thermal evolution of the Scandian hinterland, Naver nappe, northern Scotland

description: Supplementary tables: All U–Pb-trace element data.
DOI: 10.6084/m9.figshare.7946027.v1
title: Supplementary tables. Thermal evolution of the Scandian hinterland, Naver nappe, northern Scotland

description: Supplementary tables: All U–Pb-trace element data.
DOI: 10.6084/m9.figshare.7946012
title: gVCF_NA18536

description: 1000 Genomes gVCF mapped to hs37d5 for NA18536. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946009
title: gVCF_NA20814

description: 1000 Genomes gVCF mapped to hs37d5 for NA20814. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946012.v1
title: gVCF_NA18536

description: 1000 Genomes gVCF mapped to hs37d5 for NA18536. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7946009.v1
title: gVCF_NA20814

description: 1000 Genomes gVCF mapped to hs37d5 for NA20814. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.25375/uct.7946000
title: National Climate Change Profiles:Enhancing the capacity of African countries to use climate information to inform decision making

description: This national climate change profile, a series of 25 national-level assessments, is the product of the African Development Bank-led project “Enhancing the capacity of African countries to use climate information to inform decision-making and implement NDCs”,<br>The ACDI, ERC and CSAG co-produced high quality, useful climate profiles for up to 25 African countries. This process involved consistent engagement throughout with the AfDB team, as well as a Scoping and Piloting Phases to support the development of a robust methodology for producing actionable and defendable climate profiles. The purpose of these climate profiles is to support AfDB decision-making in various ways, and the profiles are therefore tailored to support existing screening tools such as the Climate Safeguards System (CSS), and associated Modules (such as the Adaptation Review and Evaluations Procedures Modules, and Adaptation Factsheet Building). The process was designed to deliver data and the interpretation of that data that will be useful for facilitating decision making at a range of decision-making levels, including development of Country Strategy Papers, sector policies, analytical work and project level decision-making.<br>
DOI: 10.6084/m9.figshare.12880052.v1
title: Additional file of Functional genomics of the horn fly, Haematobia irritans (Linnaeus, 1758)

description: Additional file of Functional genomics of the horn fly, Haematobia irritans (Linnaeus, 1758)
DOI: 10.6084/m9.figshare.12880055.v1
title: Additional file 1 of Functional genomics of the horn fly, Haematobia irritans (Linnaeus, 1758)

description: Additional file 1:Table S1. Assembling and analysis of H. irritans high quality ESTs. (XLS 502 KB)
DOI: 10.25375/uct.7946000.v1
title: National Climate Change Profiles:Enhancing the capacity of African countries to use climate information to inform decision making

description: This national climate change profile, a series of 25 national-level assessments, is the product of the African Development Bank-led project “Enhancing the capacity of African countries to use climate information to inform decision-making and implement NDCs”,<br>The ACDI, ERC and CSAG co-produced high quality, useful climate profiles for up to 25 African countries. This process involved consistent engagement throughout with the AfDB team, as well as a Scoping and Piloting Phases to support the development of a robust methodology for producing actionable and defendable climate profiles. The purpose of these climate profiles is to support AfDB decision-making in various ways, and the profiles are therefore tailored to support existing screening tools such as the Climate Safeguards System (CSS), and associated Modules (such as the Adaptation Review and Evaluations Procedures Modules, and Adaptation Factsheet Building). The process was designed to deliver data and the interpretation of that data that will be useful for facilitating decision making at a range of decision-making levels, including development of Country Strategy Papers, sector policies, analytical work and project level decision-making.<br>
DOI: 10.6084/m9.figshare.7945976
title: gVCF_NA18535

description: 1000 Genomes gVCF mapped to hs37d5 for NA18535. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.12880055
title: Additional file 1 of Functional genomics of the horn fly, Haematobia irritans (Linnaeus, 1758)

description: Additional file 1:Table S1. Assembling and analysis of H. irritans high quality ESTs. (XLS 502 KB)
DOI: 10.6084/m9.figshare.7945955
title: gVCF_NA20813

description: 1000 Genomes gVCF mapped to hs37d5 for NA20813. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7945976.v1
title: gVCF_NA18535

description: 1000 Genomes gVCF mapped to hs37d5 for NA18535. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7945955.v1
title: gVCF_NA20813

description: 1000 Genomes gVCF mapped to hs37d5 for NA20813. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7945928
title: Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (<i>Elaeis guineensis</i>) canopy segmentation and height estimation

description: The vast size of oil palm (<i>Elaeis guineensis</i>) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and &gt;75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (<i>R<sup>2</sup> </i>= 0.63) for 7 year old and 0.45 m (<i>R<sup>2</sup> </i>= 0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (&gt;75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.
DOI: 10.6084/m9.figshare.7945925
title: gVCF_NA18534

description: 1000 Genomes gVCF mapped to hs37d5 for NA18534. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7945925.v1
title: gVCF_NA18534

description: 1000 Genomes gVCF mapped to hs37d5 for NA18534. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7945928.v1
title: Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (<i>Elaeis guineensis</i>) canopy segmentation and height estimation

description: The vast size of oil palm (<i>Elaeis guineensis</i>) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and &gt;75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (<i>R<sup>2</sup> </i>= 0.63) for 7 year old and 0.45 m (<i>R<sup>2</sup> </i>= 0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (&gt;75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.