cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13626371
title: UK Atittudes Towards Low-Carbon Domestic Heating Survey 2023-24

description: Updated version of the dataset following discovery of errors in DSR questions in SP module.
DOI: 10.15468/dl.senjdq
title: Occurrence Download

description: A dataset containing 139 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-100600.0",
 "Country is Spain",
 "Geometry POLYGON((-9.43512 43.04076,-9.37413 41.68901,-6.44677 41.78003,-7.11763 39.79473,-7.05664 37.84682,-7.17861 36.19135,-4.37323 35.79661,0.93261 38.08722,0.81064 40.02863,4.10391 41.78003,3.67701 42.36852,-3.94632 43.74978,-9.13019 44.18871,-9.43512 43.04076))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (ada19a05-4216-44c7-aa4f-6e7177bcb0ac, 7338b4d9-d75b-4edb-b238-b09f52397f49, 0c392aa0-a452-4254-be5c-e49dd60e9b02, 7b4f2f30-a456-11d9-8049-b8a03c50a862)",
 "TaxonKey is Myocastor coypus"
 ]
}

 The dataset includes 139 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022407-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tudnbx
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vaccinium kunthianum",
 "BasisOfRecord is one of (Specimen, Human Observation, Occurrence evidence, Material sample, Living Specimen)",
 "HasCoordinate is true",
 "Country is one of (Canada, United States of America, Mexico, Guatemala, Honduras, El Salvador, Nicaragua, Costa Rica, Panama)"
 ]
}

 The dataset includes 47 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022408-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28678325.v4
title: Supplement: Post-ER proteolysis by K48-ubiquitin-activated proteases

description: Polyubiquitin chains, linked through K48 or K63 of ubiquitin, target membrane proteins in the secretory system to degradative pathways. Whether these linkage isomers are functionally interchangeable is unclear. Here, we show that for post-endoplasmic reticulum (ER) proteins, K63-linked polyubiquitination induces multivesicular bodies (MVBs) sorting and lysosomal degradation. In contrast, K48-linked polyubiquitination induces shearing from the membrane. Substrates are cleaved by the proteasome and by two ubiquitin-activated proteases: Ddi1, a conserved cytosolic ubiquilin that generates cytosolic fragments, and Rbd2, an intramembrane rhomboid protease that produces lumenal fragments. Rbd2 localizes to Golgi/endosomes but also acts on ubiquitinated substrates at the vacuolar membrane. Ddi1’s catalytic core, the HDD-RVP domain, binds ubiquitin directly and is sufficient for ubiquitin-dependent proteolysis. Its activity is amplified by Ddi1’s auxiliary ubiquitin binding domains: an atypical UBL domain and a UBA domain. These findings demonstrate that polyubiquitin chains linked by different residues encode distinct degradative fates for post-ER proteins, and reveals two proteases that target ubiquitinated integral membrane cargo.
DOI: 10.7910/dvn/hxsed6
title: Adaptive Multi-Interval Scale (AMIS): Open-Source Software, Source Data and Results for Normalizing and Comparing Raw & Aggregated Metrics

description: This dataset contains the complete resources for applying the Adaptive Multi-Interval Scale (AMIS) method to normalize and compare heterogeneous data. The AMIS method addresses the fundamental problem of integrating indicators measured in fundamentally incomparable scales. It is specifically designed to work with both raw individual measurements and aggregated data (frequency distributions, summary tables).

The dataset provides open-source Python software that implements the full AMIS pipeline: from data loading and transformation to adaptive normalization and the creation of matching tables. It includes tested algorithms for converting aggregated data into a normalized form and a robust inverse transformation mechanism.
The dataset ensures full reproducibility of the research and provides a practical toolkit for interdisciplinary data integration and analysis.
description: The associated methodological article provides a complete description of the AMIS method, including its mathematical foundation, algorithmic implementation, and software design. This dataset serves as the practical implementation and validation materials for that article.
DOI: 10.5281/zenodo.17791614
title: Earth.Org.UK (EOU) general BibTeX bibliography snapshot

description: General public bibliography for EOU (Earth.Org.UK) and related research, mainly focussed on fixing climate change through decarbonising UK home heating.
DOI: 10.5281/zenodo.10205024
title: Earth.Org.UK (EOU) general BibTeX bibliography snapshot

description: General public bibliography for EOU (Earth.Org.UK) and related research, mainly focussed on fixing climate change through decarbonising UK home heating.
DOI: 10.5281/zenodo.17791300
title: 27th Nov Triggering on Gamma

description: same setup but swapped the trigger to the gamma channel
DOI: 10.5281/zenodo.17791301
title: 27th Nov Triggering on Gamma

description: same setup but swapped the trigger to the gamma channel
DOI: 10.5281/zenodo.17058560
title: Code and Data: Efficient multiscale simulations of incremental sheet forming using machine learning surrogate models for crystal plasticity

description: Code and data for "Efficient multiscale simulations of incremental sheet forming using machine learning surrogate models for crystal plasticity" by John S. Weeks and Aaron P. Stebner at Georgia Institute of Technology. Journal article DOI: https://doi.org/10.1007/s40192-025-00427-0

Multiscale crystal plasticity modeling of metal forming is expensive due to complex loading conditions and high-cost microscale models. However, these simulations can be significantly accelerated through use of low-cost machine learning surrogate models. This work develops machine learning surrogate models for the constitutive response and texture evolution of a microscale crystal plasticity model using recurrent neural networks, and embeds them into efficient multiscale simulations of single-point incremental forming that demonstrate up to a 63.6x increase in speed compared to conventional techniques. Two concurrent multiscale crystal plasticity simulation workflows are demonstrated for random textured Al5052-H32 with truncated pyramid and truncated cone forming paths which show good agreement between results of forming force, thickness variation, and texture evolution. Surrogate models for the microscale constitutive response are embedded and take strain path history inputs from macroscale loading conditions and output deviatoric stresses at each material point. Surrogate models for texture evolution take resultant plastic strain history inputs and output a reduced order representation of texture using generalized spherical harmonic coefficients and principal component analysis. These workflows permit extraction of the full-field time-history response for texture evolution over the entire formed part which enables multiscale trade studies using crystal plasticity simulations as well as optimization of local microstructures in various forming applications.
DOI: 10.5281/zenodo.17058561
title: Code and Data: Efficient multiscale simulations of incremental sheet forming using machine learning surrogate models for crystal plasticity

description: Code and data for "Efficient multiscale simulations of incremental sheet forming using machine learning surrogate models for crystal plasticity" by John S. Weeks and Aaron P. Stebner at Georgia Institute of Technology. Journal article DOI: https://doi.org/10.1007/s40192-025-00427-0

Multiscale crystal plasticity modeling of metal forming is expensive due to complex loading conditions and high-cost microscale models. However, these simulations can be significantly accelerated through use of low-cost machine learning surrogate models. This work develops machine learning surrogate models for the constitutive response and texture evolution of a microscale crystal plasticity model using recurrent neural networks, and embeds them into efficient multiscale simulations of single-point incremental forming that demonstrate up to a 63.6x increase in speed compared to conventional techniques. Two concurrent multiscale crystal plasticity simulation workflows are demonstrated for random textured Al5052-H32 with truncated pyramid and truncated cone forming paths which show good agreement between results of forming force, thickness variation, and texture evolution. Surrogate models for the microscale constitutive response are embedded and take strain path history inputs from macroscale loading conditions and output deviatoric stresses at each material point. Surrogate models for texture evolution take resultant plastic strain history inputs and output a reduced order representation of texture using generalized spherical harmonic coefficients and principal component analysis. These workflows permit extraction of the full-field time-history response for texture evolution over the entire formed part which enables multiscale trade studies using crystal plasticity simulations as well as optimization of local microstructures in various forming applications.
DOI: 10.3535/mks-nz3-fl0
title: Leptuca cumulanta Crane 1943

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/ycd-xj4-5k2
title: Tubuca demani (Ortmann 1897)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/0kk-xc7-0v1
title: Gelasimus vocans

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/hsd-0de-v23
title: Tubuca hirsutimanus George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/v86-e8h-ecz
title: Mictyris longicarpus Latreille 1806

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/z6e-h3m-8bh
title: Specimen from Senckenberg Research Institute and Natural History Museum Frankfurt/M

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/bd7-psq-17s
title: Specimen from Senckenberg Research Institute and Natural History Museum Frankfurt/M

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/fbe-5s7-hbz
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/2sg-s4a-gz4
title: Uca (Minuca) argillicola Crane 1941

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/zqw-7g6-72s
title: Ligia exotica Roux 1828

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.5281/zenodo.17709135
title: Chapter_4_supplementary_tables
DOI: 10.3535/ffs-ejh-83b
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/wa3-yf9-ga4
title: Uca tangeri (Eydoux 1835)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.5281/zenodo.16093644
title: Chapter_4_supplementary_tables
DOI: 10.3535/r4e-crc-lnc
title: Uca tangeri (Eydoux 1835)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/d5t-80s-w6w
title: Gelasimus vocans

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/wdq-y5q-abn
title: Mictyris livingstonei MacNeill 1926

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.17026/ar/oovvxd
title: PAN-00178605 - ring (unspecified)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00178605&lt;/p&gt;
description: Modified: 2025-08-18T18:00:06+02:00
DOI: 10.3535/3ql-rfp-fcz
title: Uca tangeri (Eydoux 1835)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/hqj-p44-5yp
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/4zf-xct-xe9
title: Uca (Minuca) burgersi Holthuis 1967

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/5hz-dpv-yfq
title: Uca (Minuca) galapagensis Rathbun 1902

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/491-1xv-kss
title: Tubuca dussumieri (H. Milne Edwards 1852)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/j0g-tz9-e01
title: Ocypode fabricii H. Milne-Edwards 1837

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/pch-0zp-phe
title: Uca (Minuca) galapagensis Rathbun 1902

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/b9y-76l-ewh
title: Uca tangeri (Eydoux 1835)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/sb2-g8x-zbe
title: Mictyris longicarpus Latreille 1806

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/hjn-840-9qe
title: Gelasimus vocans

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/xk4-v3w-eqe
title: Uca heteropleura (Smith 1870)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/3yk-2da-gsp
title: Cyrtograpsus

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/nd9-mjw-n5k
title: Tubuca hirsutimanus George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/992-1xx-z9y
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/x81-qqt-wrq
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/w99-2fx-hbf
title: Tubuca elegans George & Jones 1982

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/56g-919-d73
title: Specimen from Senckenberg Research Institute and Natural History Museum Frankfurt/M

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/t4p-nyk-aqs
title: Tubuca demani (Ortmann 1897)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/2m1-w4l-77s
title: Mictyris sp.

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/9me-h7x-yq7
title: Tubuca demani (Ortmann 1897)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.5281/zenodo.17784771
title: Region-Specific Transcriptional Signatures of Brain Aging in the Absence of Neuropathology at the Single-cell Level

description: Single nuclear RNA-sequencing was performed on human post-mortem brain tissue from four distinct regions that have implications for neurodegenerative diseases, including Alzheimer's and Parkinson’s diseases. Younger, aged 20s to 30s, and middle-aged and older, aged 60s to 80s, adults were used to identify changes in cell populations and transcriptomes during aging.

Files included:aging.glmmtmb_age_diffs.csv.gz - All results modeled using glmmTMBaging.glmmtmb_age_diffs_fdr.csv.gz - glmmTMB results with a statistically significant result

Column headers for the glmmtmb results are:feature - gene nameintercept - regression interceptestimate - regression coefficientstd.error - standard errorstatistic - regression test statisticp.value - regression p-valuetissue - the cell-type (varying specificty, use in combinationfor type column)type - type for 'tissue' column; 'broad_celltype', 'region_broad_celltype', or 'specific_celltype'fdr_bh - B&H FDR multiple-test corrected p-value

aging.diffxpy_age_diffs.csv.gz - All results using diffxpy differential expressionaging.diffxpy_age_diffs_fdr.csv.gz - diffxpy results with a statistically significant result

Column headers for the diffxpy results are:gene - gene namepval - p-value for differential expressionqval - q-value for differential expression; multiple-test corrected p-valuelog2fc - log base 2 fold change in expressionmean -zero_mean -zero_variance -tissue - the cell-type (varying specificty, use in combinationfor type column)type - type for 'tissue' column; 'broad_celltype', 'region_broad_celltype', or 'specific_celltype'fdr_bh - B&H FDR cmultiple-test orrected p-value

aging.h5ad - AnnData containing the demultiplexed unprocessed/non-transformed quantified expression data.

aging.pegasus.leiden_085.subclustered.h5ad - AnnData containing the fully processed, transformed, clustered, and cell-type labeled expression data.
DOI: 10.5281/zenodo.7803697
title: Region-Specific Transcriptional Signatures of Brain Aging in the Absence of Neuropathology at the Single-cell Level

description: Single nuclear RNA-sequencing was performed on human post-mortem brain tissue from four distinct regions that have implications for neurodegenerative diseases, including Alzheimer's and Parkinson’s diseases. Younger, aged 20s to 30s, and middle-aged and older, aged 60s to 80s, adults were used to identify changes in cell populations and transcriptomes during aging.

Files included:aging.glmmtmb_age_diffs.csv.gz - All results modeled using glmmTMBaging.glmmtmb_age_diffs_fdr.csv.gz - glmmTMB results with a statistically significant result

Column headers for the glmmtmb results are:feature - gene nameintercept - regression interceptestimate - regression coefficientstd.error - standard errorstatistic - regression test statisticp.value - regression p-valuetissue - the cell-type (varying specificty, use in combinationfor type column)type - type for 'tissue' column; 'broad_celltype', 'region_broad_celltype', or 'specific_celltype'fdr_bh - B&H FDR multiple-test corrected p-value

aging.diffxpy_age_diffs.csv.gz - All results using diffxpy differential expressionaging.diffxpy_age_diffs_fdr.csv.gz - diffxpy results with a statistically significant result

Column headers for the diffxpy results are:gene - gene namepval - p-value for differential expressionqval - q-value for differential expression; multiple-test corrected p-valuelog2fc - log base 2 fold change in expressionmean -zero_mean -zero_variance -tissue - the cell-type (varying specificty, use in combinationfor type column)type - type for 'tissue' column; 'broad_celltype', 'region_broad_celltype', or 'specific_celltype'fdr_bh - B&H FDR cmultiple-test orrected p-value

aging.h5ad - AnnData containing the demultiplexed unprocessed/non-transformed quantified expression data.

aging.pegasus.leiden_085.subclustered.h5ad - AnnData containing the fully processed, transformed, clustered, and cell-type labeled expression data.
DOI: 10.6084/m9.figshare.30763082
title: <b>File 1 — Training Dataset (PubChem + ChEMBL)</b><b> for Dr Lungker</b>

description: <b>Article Title:</b> <i>DrLungker: A Deep Ensemble Learning Framework for Predicting Anti-Lung Cancer Compound Activity and Validating Multitarget Potency through WaterMap, DFT, MD Simulations, and MM-GBSA Analysis</i><br><b>Published in:</b> <i>Advanced Theory and Simulations</i><br><b>Manuscript DOI:</b> https://doi.org/10.1002/adts.202501550<br><b>More Information:</b> GitHub Repository<b>Description</b>This dataset (<code>DrLungker_Dataset.csv</code>) contains the fully curated molecular data used to train the <b>DrLungker deep ensemble learning framework</b> for predicting anti-lung cancer compound activity.<b>Sources:</b> PubChem and ChEMBL lung cancer bioassays<b>Processing:</b> Structure standardization, duplicate removal, descriptor generation using AlvaDesc and QikProp, and rigorous quality filtering<b>Contents:</b> 26,396 unique compounds, each encoded with 5,883 molecular descriptors<b>Usage:</b> Training the hybrid <b>ResNet–FNN–LSTM ensemble</b> using Averaging, Majority Voting, and Stacking techniquesThis dataset ensures <b>full reproducibility</b> of the DrLungker model and can be used for benchmarking, validation, and downstream computational drug-discovery applications.
DOI: 10.6084/m9.figshare.30763082.v4
title: <b>File 1 — Training Dataset (PubChem + ChEMBL)</b><b> for Dr Lungker</b>

description: <b>Article Title:</b> <i>DrLungker: A Deep Ensemble Learning Framework for Predicting Anti-Lung Cancer Compound Activity and Validating Multitarget Potency through WaterMap, DFT, MD Simulations, and MM-GBSA Analysis</i><br><b>Published in:</b> <i>Advanced Theory and Simulations</i><br><b>Manuscript DOI:</b> https://doi.org/10.1002/adts.202501550<br><b>More Information:</b> GitHub Repository<b>Description</b>This dataset (<code>DrLungker_Dataset.csv</code>) contains the fully curated molecular data used to train the <b>DrLungker deep ensemble learning framework</b> for predicting anti-lung cancer compound activity.<b>Sources:</b> PubChem and ChEMBL lung cancer bioassays<b>Processing:</b> Structure standardization, duplicate removal, descriptor generation using AlvaDesc and QikProp, and rigorous quality filtering<b>Contents:</b> 26,396 unique compounds, each encoded with 5,883 molecular descriptors<b>Usage:</b> Training the hybrid <b>ResNet–FNN–LSTM ensemble</b> using Averaging, Majority Voting, and Stacking techniquesThis dataset ensures <b>full reproducibility</b> of the DrLungker model and can be used for benchmarking, validation, and downstream computational drug-discovery applications.