cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14955641
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver - ST FASTQ Female rAAV9-Treated Animal 352

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the spatial transcriptomics  FASTQ files of the female rAAV9-treated animal from the sample SN326_B1_352.
DOI: 10.15468/dl.evatvr
title: Occurrence Download

description: A dataset containing 71634 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rangifer tarandus (Linnaeus, 1758)"
 ]
}

 The dataset includes 71634 records from 191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006967-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qwq6g8
title: Occurrence Download

description: A dataset listing the 1520 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-364.15942 50.37569,-364.13576 50.37569,-364.13576 50.39477,-364.15942 50.39477,-364.15942 50.37569))"
 ]
}

 The dataset's 1520 records were derived from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007001-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14965702
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver - snRNA-seq Cellranger and Cellbender Output

description: You can find here the snRNA-seq files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq cellranger and cellbender outputs of all samples.
DOI: 10.5281/zenodo.14965703
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver - snRNA-seq Cellranger and Cellbender Output

description: You can find here the snRNA-seq files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq cellranger and cellbender outputs of all samples.
DOI: 10.15468/dl.cdna3u
title: Occurrence Download

description: A dataset containing 2359 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Montanelia sorediata (Ach.) Divakar, A.Crespo, Wedin & Essl."
 ]
}

 The dataset includes 2359 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007011-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.71682/10549300
title: Evaluating sustainable cropping systems: A comparative study of agricultural practices in Central Malawi

description: This database contains grain, biomasss and nutritional yield data from on-farm trials in Kasungu, Mchinji, and Lilongwe districts of central Malawi, conducted over three cropping seasons (2014–15 to 2016-17). The trials tested sustainable and resilient cropping systems, including Conservation Agriculture (CA) with minimum tillage, glyphosate herbicide, and maize-legume rotations, compared to conventional ridge-and-furrow sole maize (True farmer practice). The experiments were conducted on 24 farms, with each farm serving as one replicate. Data collected included grain yield, biomass, protein, and energy yields, providing insights into system performance and sustainability.
DOI: 10.71682/10549300/1
title: grain_and_nutritional_yields_v00.xlsx
DOI: 10.15468/dl.wqzztr
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "TaxonKey is Anchusa puechii Valdés",
 "Year 1972-2025"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007013-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1128358
title: Data from a Trial Trench Evaluation South of Chalfont Lane, West Hyde, Hertfordshire and Buckinghamshire, 2017-2018 (HS2 Phase One)

description: This collection comprises of site records, images and an archive decision sheet from a trial trench evaluation carried out by COPA (Cotswold Oxford Pre-Construct Archaeology) on land located south of Chalfont Lane, West Hyde, in Hertfordshire and Buckinghamshire between October 2017 and January 2018.
DOI: 10.5281/zenodo.14966448
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 101

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 101.
DOI: 10.5281/zenodo.14966449
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 101

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 101.
DOI: 10.5066/p148rtgv
title: Laboratory optical measurements to assess long-term changes in water-extractable soil organic matter, U.S. Great Plains

description: Carbon (C) and nitrogen (N) extracted from soil with water are associated with available plant nutrients and microbial activity, but information about how C and N have changed over time in the U.S. Great Plains is sparse. Optical measurements of absorbance and fluorescence were used to compare dissolved organic matter (DOM) extracted from historic (1947) and contemporary (2018) soil samples collected from Moccasin, MT, Akron, CO, and Big Spring, TX. Extracts were prepared at two temperatures (20 and 80 degrees Celsius) associated with different DOM pools to evaluate changes to soil DOM composition after 71 years of dryland farming practices. Together, water extracts and subsequent optical methods can produce insights into long term patterns of soil change while conserving limited and irreplaceable physical samples of archived soil.
DOI: 10.6084/m9.figshare.28566698
title: Data from: Patterns of water-extractable soil organic matter in the US Great Plains: Insights from the Haas Soil Archive

description: Carbon (C) and nitrogen (N) extracted from soil with water are associated with available plant nutrients and microbial activity but information about how they have changed over time in the U.S Great Plains is sparse. We used cool (20 oC) and hot (80 oC) water extracts from historic (1947) and current (2018) soil samples collected at Moccasin, MT, Akron, CO, and Big Spring, TX to evaluate changes to extractable C and N and optical properties after 71 years of dryland cropping. Together, water extracts and optical methods can produce insights into long-term changes in labile soil organic matter while conserving physical samples of archived soil.
DOI: 10.15468/f47a5v
title: RBINS Bryozoa collection

description: The RBINS has important material from the North Sea, including historical collections and recent material, as well as material from freshwater habitats. A lot of material was collected during the different expeditions that the RBINS has been involved in. The Antarctic collections, in particular, are rich in type specimens.
DOI: 10.6084/m9.figshare.28590161
title: Cloudy catshark shark gene models

description: Gene models inferred with Braker3 on the genome assembly sScyTor2.1
DOI: 10.15468/dl.st9yca
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudophilothrips varicornis (Hood, 1912)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007014-250310093411724/datasets/export for details.
DOI: 10.5281/zenodo.10846543
title: Global patterns of soil organic carbon dynamics in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

description: The file name is “GE_β.tif”, GE represents global ecosystems, which including cropland(CL), grassland(GL), and forestland(FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.
description: Determining the distribution of soil organic carbon (SOC) in subsoil (20–100 cm depth) is important with respect to the global C cycle and warming mitigation. However, significant knowledge gaps remain regarding the spatiotemporal dynamics of SOC within this layer. By integrating traditional depth functions with machine learning approaches, we quantified soil β values, which represent the relative rate of decline in SOC density with depth, and provided high-resolution assessments of SOC dynamics across global ecosystems, including cropland, grassland, and forestland. The estimated subsoil SOC densities were 62 Mg ha⁻¹ (95% CI: 52-73) for cropland, 70 Mg ha⁻¹ (95% CI: 57-83) for grassland, and 97 Mg ha⁻¹ (95% CI: 80-117) for forestland. SOC density exhibited a consistent decline with depth, ranging from 30 Mg ha⁻¹ to 5 Mg ha⁻¹ in cropland, 32 Mg ha⁻¹ to 7 Mg ha⁻¹ in grassland, and 40 Mg ha⁻¹ to 13 Mg ha⁻¹ in forestland, across 20 cm depth increments from 20 to 100 cm. The estimated global subsoil SOC stock was 803 Pg C, with cropland, grassland, and forestland contributing 74 Pg C, 181 Pg C, and 547 Pg C, respectively. On average, 57% of this carbon was stored within the top 0-100 cm of the soil profile. This study provides information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution across global ecosystems, providing a scientific basis for future studies pertaining to Earth system models.
DOI: 10.5281/zenodo.14787023
title: Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

description:  The file named “Rawdata.xlsx” contains data sourced from the literature. The file name is “GE_β.tif”, GE represents global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.
description: Determining the distribution of soil organic carbon (SOC) in subsoil (depth of 20–100 cm) is important with respect to the global C cycle and warming mitigation. However, there is still a huge knowledge gap in the dynamics of spatiotemporal changes in SOC in this layer. Combining traditional depth functions and machine-learning methods, we achieved soil β values (the relative rate of decrease in the SOC density with soil depth), and SOC dynamics at high resolution for global ecosystems (cropland, grassland, and forestland). First, we quantified the spatial variability characteristics of soil β values, which indicated the rate at which SOC density decreases with soil depth, and driving factors by analyzing 17984 soil profiles (0–100 cm) of globally distributed field observations. Then, based on multiple environmental variables and soil profile data, we mapped the grid-level soil β values with machine-learning approaches. Lastly, we evaluated the SOC density spatial distribution in different soil layers to determine the subsoil SOC stocks of various ecosystems. The subsoil SOC density values of cropland, grassland, and forestland were 62 Mg ha–1 (95% CI: 52-73), 70 Mg ha–1 (95% CI: 57-83), and 97 Mg ha–1 (95% CI: 80-117), respectively. SOC density decreases with increasing depth, ranging from 30 Mg ha⁻¹(95% CI: 26-35) to 5 Mg ha⁻¹ (95% CI: 4-7) (at depth intervals of 20-100 cm, in 20 cm increments) for cropland, from 32 Mg ha⁻¹ (95% CI: 27-37) to 7 Mg ha⁻¹ (95% CI: 5-9) for grassland, and from 40 Mg ha⁻¹ (95% CI: 34-46) to 13 Mg ha⁻¹ (95% CI: 9-17) for forestland. The global subsoil SOC stock was 803 Pg C (95%CI:661-962) (cropland, grassland, and forestland were 74 Pg C (95% CI:62-88), 181 Pg C (95% CI:148-215), and 547 Pg C (95% CI:451-660)), in which an average of 56% resided in the top 0–100 cm of the soil profile. This study provides information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution across global ecosystems, providing a scientific basis for future studies pertaining to Earth system models.
DOI: 10.5281/zenodo.10846542
title: Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

description:  The file named “Rawdata.xlsx” contains data sourced from the literature. The file name is “GE_β.tif”, GE represents global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.
description: Determining the distribution of soil organic carbon (SOC) in subsoil (20–100 cm depth) is important with respect to the global C cycle and warming mitigation. However, significant knowledge gaps remain regarding the spatiotemporal dynamics of SOC within this layer. By integrating traditional depth functions with machine learning approaches, we quantified soil β values, which represent the relative rate of decline in SOC density with depth, and provided high-resolution assessments of SOC dynamics across global ecosystems, including cropland, grassland, and forestland. The estimated subsoil SOC densities were 62 Mg ha⁻¹ (95% CI: 52-73) for cropland, 70 Mg ha⁻¹ (95% CI: 57-83) for grassland, and 97 Mg ha⁻¹ (95% CI: 80-117) for forestland. SOC density exhibited a consistent decline with depth, ranging from 30 Mg ha⁻¹ to 5 Mg ha⁻¹ in cropland, 32 Mg ha⁻¹ to 7 Mg ha⁻¹ in grassland, and 40 Mg ha⁻¹ to 13 Mg ha⁻¹ in forestland, across 20 cm depth increments from 20 to 100 cm. The estimated global subsoil SOC stock was 803 Pg C, with cropland, grassland, and forestland contributing 74 Pg C, 181 Pg C, and 547 Pg C, respectively. On average, 57% of this carbon was stored within the top 0-100 cm of the soil profile. This study provides information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution across global ecosystems, providing a scientific basis for future studies pertaining to Earth system models.
DOI: 10.5281/zenodo.15019078
title: Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

description:  The file named “Rawdata.xlsx” contains data sourced from the literature. The file name is “GE_β.tif”, GE represents global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.
description: Determining the distribution of soil organic carbon (SOC) in subsoil (20–100 cm depth) is important with respect to the global C cycle and warming mitigation. However, significant knowledge gaps remain regarding the spatiotemporal dynamics of SOC within this layer. By integrating traditional depth functions with machine learning approaches, we quantified soil β values, which represent the relative rate of decline in SOC density with depth, and provided high-resolution assessments of SOC dynamics across global ecosystems, including cropland, grassland, and forestland. The estimated subsoil SOC densities were 62 Mg ha⁻¹ (95% CI: 52-73) for cropland, 70 Mg ha⁻¹ (95% CI: 57-83) for grassland, and 97 Mg ha⁻¹ (95% CI: 80-117) for forestland. SOC density exhibited a consistent decline with depth, ranging from 30 Mg ha⁻¹ to 5 Mg ha⁻¹ in cropland, 32 Mg ha⁻¹ to 7 Mg ha⁻¹ in grassland, and 40 Mg ha⁻¹ to 13 Mg ha⁻¹ in forestland, across 20 cm depth increments from 20 to 100 cm. The estimated global subsoil SOC stock was 803 Pg C, with cropland, grassland, and forestland contributing 74 Pg C, 181 Pg C, and 547 Pg C, respectively. On average, 57% of this carbon was stored within the top 0-100 cm of the soil profile. This study provides information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution across global ecosystems, providing a scientific basis for future studies pertaining to Earth system models.
DOI: 10.5281/zenodo.14967057
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 102

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 102.
DOI: 10.15468/ga5ady
title: RBINS Fish collection

description: The RBINS fish collection consists of more than 265,000 specimens. Oceanographer Gustave Gilson (director of the RBINS in 1909) started the collection during his study of the North Sea. It was the very first study of marine fauna in Belgium. The Institute has type material for 325 species. Central African fish species are well represented thanks to expeditions to the national parks in Congo. Missions by His Majesty Leopold III and RBINS biologist Jean-Pierre Gosse have also yielded many South American species.
DOI: 10.5281/zenodo.14967056
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 102

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 102.
DOI: 10.5281/zenodo.14967117
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 103

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 103
DOI: 10.5281/zenodo.14967118
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Male Control  Animal 103

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the male control animal 103
DOI: 10.15468/hsh877
title: RBINS Mammal collection

description: The RBINS mammal collection contains 63,000 specimens and includes significant osteological material (bones and skeletons). The best represented groups are bats, rodents, carnivores and primates. The Institute has type material for 27 species. The rodent collection was extended by RBINS biologist Xavier Misonne. Walter Verheyen built up the collection of fauna from Belgium and Papua New Guinea, and Jacques Verschuren put together a large collection of mammal specimens from Africa.
DOI: 10.5281/zenodo.15017287
title: Quantum Simulation of non-Abelian Lattice Gauge Theories: a variational approach to $\mathbb{D}_8$ with dynamical matter

description: This repository contains the dataset and the Python scripts used for generating Figures 6 and 11 in our paper




https://doi.org/10.48550/arXiv.2501.17863




 

The data is generated by a simulation protocol implemented in Python. 

The data contains the values of the fidelity with respect to the true ground state and the energy of the variational state as a function of the time for N = 1,2,3,4 and 5 layers of the variational circuit. In particular, the file ITE_1d_M=1_J=1_h=1.csv contains the data for the 1d model and ITE_pseudo_2d_M=1_J=1_h=1.csv for the pseudo-2d model.
DOI: 10.5281/zenodo.15017288
title: Quantum Simulation of non-Abelian Lattice Gauge Theories: a variational approach to $\mathbb{D}_8$ with dynamical matter

description: This repository contains the dataset and the Python scripts used for generating Figures 6 and 11 in our paper




https://doi.org/10.48550/arXiv.2501.17863




 

The data is generated by a simulation protocol implemented in Python. 

The data contains the values of the fidelity with respect to the true ground state and the energy of the variational state as a function of the time for N = 1,2,3,4 and 5 layers of the variational circuit. In particular, the file ITE_1d_M=1_J=1_h=1.csv contains the data for the 1d model and ITE_pseudo_2d_M=1_J=1_h=1.csv for the pseudo-2d model.
DOI: 10.5281/zenodo.14967222
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Female Control  Animal 152

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the female control animal 152.
DOI: 10.5281/zenodo.14967223
title: Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver: snRNA-seq FASTQ Female Control  Animal 152

description: You can find here the FASTQ files related to the datasets used in the publication: 

Amberg, B. et al.  Spatial Transcriptomics and Single Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

In this particular upload, you can find the snRNA-seq  FASTQ files of the female control animal 152.