cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4225/08/586ec70f080bf
title: REP_V2_R3: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted February 2014

description: Compositional turnover patterns in reptiles species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the Atlas of Living Australia current to 28th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and &gt;3 species aggregated per 9-second grid cell).  The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). This model was used in the AdaptNRM series of reports by Williams et al. (2013) and Prober et al. (2014).
DOI: 10.4225/08/586ef70d640f4
title: AMP_V2_R2: Generalised dissimilarity model of compositional turnover in amphibian species for continental Australia at 9 second resolution using ALA data extracted February 2014

description: Compositional turnover patterns in amphibian species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the Atlas of Living Australia current to 27th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and &gt;3 species aggregated per 9-second grid cell). The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). This model was used in the AdaptNRM series of reports by Williams et al. (2013) and Prober et al. (2014).
DOI: 10.5281/zenodo.10383492
title: Atrophy Pattern Maps of Frontotemporal Dementia variants (bvFTD, svPPA, pnfaPPA)

description: The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of frontotemporal dementia (FTD) patients, divided according to subtype diagnosis, against matched normal controls.

BV = behavioral-variant Frontotemporal Dementia

SV = semantic-variant Primary Progressive Aphasia

PNFA = non-fluent-variant Primary Progressive Aphasia

 

FTD map is based on NIFD data, available at: https://memory.ucsf.edu/research/studies/nifd

For more information regarding the participants and method details, see:

Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." Neurobiology of aging 111 (2022): 54-63.

Metz, Amelie, et al. "Brain Atrophy and White Matter Hyperintensities in Frontotemporal Dementia Variants and their Impact on Cognition" [Conference presentation] CCNA 2024 Partners Forum and Science Days (2024, March 19-21).
DOI: 10.5281/zenodo.1453841
title: Optimal Integration Times for Lagrangian Studies of Atmospheric Moisture Sources and Sinks

description: <strong>Lagrangian methods</strong> for estimating sources and sinks of water vapour have increased in importance in recent years, with hundreds of publications over the past decade on this topic. Results derived from these approaches are, however, very sensitive to the integration time of the trajectories used in the analysis. The most widely used integration time is that derived from the average residence time of water vapour in the atmosphere, normally considered to be around 10 days. In this database, we provide <strong>two versions</strong> of the <strong>Optimal Integration Times (OPTs) </strong>for a spatial resolution of <strong>0.25º x 0.25º in latitude and longitude</strong> using data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) for Lagrangian Studies of Atmospheric Moisture Sources and Sinks<strong>: </strong> <strong>Version 1: </strong> includes the <strong>climatological annual and monthly Optimal Integration Times (OPTs)</strong> for the period <strong>1980-2015</strong>. This database version 1 is completely described in: <strong>R. Nieto, L. Gimeno (2019) A database of optimal integration times for Lagrangian studies of atmospheric moisture sources and sinks, Scientific Data 6, 1-10, https://doi.org/10.1038/s41597-019-0068-8 </strong>and you should reference it directly. <strong>Version 2 </strong>provides the <strong>OPTs month-by-month</strong> with <strong>widespread temporal coverage</strong> from <strong>January 1980 - December 2018</strong>. The OPTs are provided as an <strong>individual monthly dataset</strong>. This version 2 is described in <strong>R. Nieto, L. Gimeno (2021) </strong>an Addendum in<strong> Scientific Data 8</strong>, 130; https://doi.org/10.1038/s41597-019-0068-8.
description: {"references": ["R. Nieto,\u00a0L. Gimeno\u00a0(2019)\u00a0A database of optimal integration times for Lagrangian studies of atmospheric moisture sources and sinks,\u00a0Scientific Data, May, Vol. 6, Pages 1-10, DOI: https://doi.org/10.1038/s41597-019-0068-8"]}
DOI: 10.5281/zenodo.6580223
title: Hereon LDIR Library for Microplastic Analysis

description: LDIR (laser direct infrared) imaging is based on a QCL source and can enable efficient and accurate microplastic analysis. However, the quality of the analysis is highly dependent on the underlying spectral library. We have expanded the manufacturer's library by IR spectra of natural particles and materials as well as various microplastic model particles and fibers. The library enables a more accurate analysis of environmental samples and it significantly reduces the need for reevaluation work. The library is optimized for marine and riverine filtration samples. However, it can certainly also be helpful regarding the analysis of other matrices.
DOI: 10.5281/zenodo.8340397
title: GLH-NDRS Centralised Data Submission Survey Results

description: Full responses from the 2023 UK CSG Diagnostic Laboratory Survey component focused on centralised data submission to PHE/NHSD and LIMS system organisation. To preserve anonymity, free-text comments are not provided.
DOI: 10.15154/1526508
title: Mispatterning and interneuron deficit in Tourette Syndrome Basal Ganglia Organoids

description: Tourette Syndrome (TS) is a neuropsychiatric disorder thought to involve a reduction of basal ganglia (BG) interneurons and malfunctioning of the BG circuitry. However, whether interneurons fail to develop or are lost postnatally remains unknown. To investigate the pathophysiology of early development in TS, induced pluripotent stem cell (iPSC)-derived BG organoids from TS patients and healthy controls were compared on multiple levels of measurement and analysis. BG organoids from TS individuals manifested an impaired medial ganglionic eminence fate and a decreased differentiation of cholinergic and GABAergic interneurons. Transcriptome analyses revealed organoid mispatterning in TS, with a preference for dorsolateral at the expense of ventromedial fates. Our results point to altered expression of GLI transcription factors downstream of the Sonic Hedgehog signaling pathway with cilia disruption at the earliest stages of BG organoid differentiation as a potential mechanism for the BG mispatterning in TS. This study uncovers early neurodevelopmental underpinnings of TS neuropathological deficits using organoids as a model system.
DOI: 10.6073/pasta/4182de376fde52e15d493fdd9f26d0c7
title: Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Beaverdam Reservoir, Virginia, USA in 2016-2022

description: We monitored water quality in Beaverdam Reservoir (Vinton, Virginia, USA, 37.31288, -79.8159) with high-frequency (10-minute and 15-minute) sensors in 2016-2022. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of 2 data sets: BVR_sensor_string_2016_2020.csv and BVR_platform_data_2020_2022.csv. BVR_sensor_string_2016_2022.csv consists of a temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by a dissolved oxygen logger at 5 m or 10 m depending on the time of year. A sonde measuring temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was deployed at ~1.5 m depth. This initial data set spans 2016 to 2020, with no additional data collection beyond the last observation. The second data set is BVR_platform_data_2020_2022.csv, with data collection still ongoing. This data set contains 1) a temperature string with 13 temperature sensors ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; 2) two oxygen sensors, one in the middle of the string and one sensor above the sediments; and 3) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 data set is also included in this 2020-2022 data set, deployed at 1.5 m below the surface. The temperature string with the thermistors, dissolved oxygen sensor, and pressure sensor are permanently fixed to the platform and water level changes around them. In the methods we describe how to add a depth measurement to each observation.
DOI: 10.6084/m9.figshare.25451908
title: The detailed results of model comparison between basline models and pepMTL in RT, CCS, MS/MS prediction

description: This is a record file of the running results of the pepMTL model in the article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", as well as the various benchmark models in RT, CCS, and MS/MS aspects. By carefully tiling the results of the run and saving the tiling results in the xlsx file.这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行结果的记录文件。通过对运行结果进行仔细整理,并将整理的结果保存在了xlsx文件中。
DOI: 10.5281/zenodo.10839124
title: The detailed results of model comparison between basline models and pepMTL in RT, CCS, MS/MS prediction

description: This is a record file of the running results of the pepMTL model in the article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", as well as the various benchmark models in RT, CCS, and MS/MS aspects. By carefully tiling the results of the run and saving the tiling results in the xlsx file.

这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行结果的记录文件。通过对运行结果进行仔细整理,并将整理的结果保存在了xlsx文件中。
DOI: 10.5281/zenodo.10839125
title: The detailed results of model comparison between basline models and pepMTL in RT, CCS, MS/MS prediction

description: This is a record file of the running results of the pepMTL model in the article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", as well as the various benchmark models in RT, CCS, and MS/MS aspects. By carefully tiling the results of the run and saving the tiling results in the xlsx file.

这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行结果的记录文件。通过对运行结果进行仔细整理,并将整理的结果保存在了xlsx文件中。
DOI: 10.6084/m9.figshare.25441627
title: Baseline model runs along with detailed result files presented in "pepMTL"

description: Within Article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", there exists a collection of executable files and detailed records of the operation results pertaining to the pepMTL model along with several benchmark models in the context of Retention Time (RT), Collision Cross Section (CCS), and Tandem Mass Spectrometry (MS/MS) methodologies. This compendium encompasses the source code for both the benchmark models and the pepMTL model itself—altered as necessary to accommodate the benchmark datasets—the specific datasets designed to execute these models effectively, the raw output files resulting from three parallel runs of each model, and corresponding log files meticulously documenting the execution history. Readers can garner extensive raw data concerning the direct comparisons between the pepMTL model and the array of benchmark models across the RT, CCS, and MS/MS domains as presented in the article. Furthermore, they have the ability to retrain these benchmark models on the designated datasets using the optimized code provided by the authors.<br>这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中,pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行文件以及详细运行结果的记录文件。该文件中包含了各个基准模型以及pepMTL模型运行的代码(有必要的话会经过作者的修改以适应基准数据集)、适应模型运行的基准数据集、模型平行运行三次后产生的原始结果文件以及运行日志记录文件。读者可以从这些数据中获取文章中关于pepMTL在RT、CCS、MS/MS方面与各个基准模型进行对比的详细原始信息,并可以通过运行作者优化后的代码将这些基准模型在基准数据集上进行模型训练。
DOI: 10.6084/m9.figshare.25441627.v2
title: Baseline model runs along with detailed result files presented in "pepMTL"

description: Within Article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", there exists a collection of executable files and detailed records of the operation results pertaining to the pepMTL model along with several benchmark models in the context of Retention Time (RT), Collision Cross Section (CCS), and Tandem Mass Spectrometry (MS/MS) methodologies. This compendium encompasses the source code for both the benchmark models and the pepMTL model itself—altered as necessary to accommodate the benchmark datasets—the specific datasets designed to execute these models effectively, the raw output files resulting from three parallel runs of each model, and corresponding log files meticulously documenting the execution history. Readers can garner extensive raw data concerning the direct comparisons between the pepMTL model and the array of benchmark models across the RT, CCS, and MS/MS domains as presented in the article. Furthermore, they have the ability to retrain these benchmark models on the designated datasets using the optimized code provided by the authors.<br>这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中,pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行文件以及详细运行结果的记录文件。该文件中包含了各个基准模型以及pepMTL模型运行的代码(有必要的话会经过作者的修改以适应基准数据集)、适应模型运行的基准数据集、模型平行运行三次后产生的原始结果文件以及运行日志记录文件。读者可以从这些数据中获取文章中关于pepMTL在RT、CCS、MS/MS方面与各个基准模型进行对比的详细原始信息,并可以通过运行作者优化后的代码将这些基准模型在基准数据集上进行模型训练。
DOI: 10.6084/m9.figshare.25441627.v1
title: Baseline model runs along with detailed result files presented in "pepMTL"

description: Within Article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", there exists a collection of executable files and detailed records of the operation results pertaining to the pepMTL model along with several benchmark models in the context of Retention Time (RT), Collision Cross Section (CCS), and Tandem Mass Spectrometry (MS/MS) methodologies. This compendium encompasses the source code for both the benchmark models and the pepMTL model itself—altered as necessary to accommodate the benchmark datasets—the specific datasets designed to execute these models effectively, the raw output files resulting from three parallel runs of each model, and corresponding log files meticulously documenting the execution history. Readers can garner extensive raw data concerning the direct comparisons between the pepMTL model and the array of benchmark models across the RT, CCS, and MS/MS domains as presented in the article. Furthermore, they have the ability to retrain these benchmark models on the designated datasets using the optimized code provided by the authors.<br>这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中,pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行文件以及详细运行结果的记录文件。该文件中包含了各个基准模型以及pepMTL模型运行的代码(有必要的话会经过作者的修改以适应基准数据集)、适应模型运行的基准数据集、模型平行运行三次后产生的原始结果文件以及运行日志记录文件。读者可以从这些数据中获取文章中关于pepMTL在RT、CCS、MS/MS方面与各个基准模型进行对比的详细原始信息,并可以通过运行作者优化后的代码将这些基准模型在基准数据集上进行模型训练。
DOI: 10.5281/zenodo.10803553
title: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2

description: The dataset named SEBAL evapotranspiration in China (SEBAL ET)  characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance.  SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.
DOI: 10.5281/zenodo.10803216
title: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1

description: The dataset named SEBAL evapotranspiration in China (SEBAL ET)  characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance.  SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.
DOI: 10.5281/zenodo.10844754
title: Gene trees and corresponding nucleotide/protein alignments across 17 species (including 13 fishes)

description: This archive contains gene trees and corresponding nucleotide/protein alignments across 17 species (including 13 fishes).
description: This archive contains the following files and folders:

-Gene_to_File.csv

This file provides the files in which gene trees and nucleotide/protein alignments can be found for each gene (based on its longest transcript).

-trees.tar.gz

This folder contains all 15919 trees reported in FEVER.

-prot_aln.tar.gz

This folder contains all protein alignments used for gene tree reconstruction.

-nuc_aln.tar.gz

This folder contains all nucleotide alignments used for gene tree reconstruction.
DOI: 10.5281/zenodo.10844755
title: Gene trees and corresponding nucleotide/protein alignments across 17 species (including 13 fishes)

description: This archive contains gene trees and corresponding nucleotide/protein alignments across 17 species (including 13 fishes).
description: This archive contains the following files and folders:

-Gene_to_File.csv

This file provides the files in which gene trees and nucleotide/protein alignments can be found for each gene (based on its longest transcript).

-trees.tar.gz

This folder contains all 15919 trees reported in FEVER.

-prot_aln.tar.gz

This folder contains all protein alignments used for gene tree reconstruction.

-nuc_aln.tar.gz

This folder contains all nucleotide alignments used for gene tree reconstruction.