cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17778963
title: Cannabidiol inhibits both human Kv7.1 and Kv7.1/KCNE1 channels through distinct sites

description: Molecular dynamics data for the paper: "Cannabidiol inhibits both human Kv7.1 and Kv7.1/KCNE1 channels through distinct sites."

Data set:



Q1_6UZZ_APO (Simulation of apo Kv7.1 structure)

Q1_6UZZ_CBD (Simulation of CBD-bound Kv7.1 structure)

Q1_chai1_CBD (Simulation of CBD-bound chai-1 predicted Kv7.1 structure)

Q1E1_6V00_APO (Simulation of apo Kv7.1/KCNE1 model)

Q1E1_6V00_CBD (Simulation of CBD-bound Kv7.1/KCNE1 model)

Q1E1_chai1_CBD (Simulation of CBD-bound chai-1 predicted Kv7.1/KCNE1 structure)


The protein in each simulation reflects the transmembrane section.

The most populated binding pose clusters for CBD in the Kv7.1 (Q1_6UZZ_CBD) structure and the Kv7.1/KCNE1 (Q1E1_6V00_CBD) model are provided.Water, ions and lipids have been removed; only every 10th frame of the simulation trajectories is kept.
DOI: 10.25909/5dc8e3e65e162
title: Fatigue_Pain_Data.xlsx

description: Data from Fatigue/Pain Study of Physiotherapists<br>
DOI: 10.6084/m9.figshare.30890858
title: Automated Change Impact Analysis for Deep Learning Programs - Dataset

description: Deep learning is the core technology underlying many Artificial Intelligence (AI) applications. Maintainingdeep learning software programs presents unique challenges due to their complex, interdependent structures.Traditional change impact analysis methods, while effective for conventional software, fall short whenthey are applied to deep learning programs due to the intricate interdependencies within neural networks,particularly those related to the unique architectural layers and hyperparameters. This paper presents anovel approach to change impact analysis which is specifically designed for deep learning programs. Weintroduce a comprehensive taxonomy that systematically categorizes changes in deep learning architectures,hyperparameters, and regularization techniques. Our approach leverages this taxonomy to map changesautomatically, thereby enabling structured and efficient impact analysis in complex deep learning programs.To predict the potential impact of initial changes, our framework incorporates co-change pattern mining toidentify and generate co-change rules, capturing dependencies based on historical co-change patterns. Wealso designed domain-expert rules derived from deep learning literature and best practices. Our evaluationresults demonstrate that our co-change mining approach effectively balances recall and precision, with theintegration of co-change patterns and domain-expert rules offering additional complementary benefits.
DOI: 10.6084/m9.figshare.30890858.v1
title: Automated Change Impact Analysis for Deep Learning Programs - Dataset

description: Deep learning is the core technology underlying many Artificial Intelligence (AI) applications. Maintainingdeep learning software programs presents unique challenges due to their complex, interdependent structures.Traditional change impact analysis methods, while effective for conventional software, fall short whenthey are applied to deep learning programs due to the intricate interdependencies within neural networks,particularly those related to the unique architectural layers and hyperparameters. This paper presents anovel approach to change impact analysis which is specifically designed for deep learning programs. Weintroduce a comprehensive taxonomy that systematically categorizes changes in deep learning architectures,hyperparameters, and regularization techniques. Our approach leverages this taxonomy to map changesautomatically, thereby enabling structured and efficient impact analysis in complex deep learning programs.To predict the potential impact of initial changes, our framework incorporates co-change pattern mining toidentify and generate co-change rules, capturing dependencies based on historical co-change patterns. Wealso designed domain-expert rules derived from deep learning literature and best practices. Our evaluationresults demonstrate that our co-change mining approach effectively balances recall and precision, with theintegration of co-change patterns and domain-expert rules offering additional complementary benefits.
DOI: 10.15468/dl.s8payn
title: Occurrence Download

description: A dataset containing 9291 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Mareca falcata"
 ]
}

 The dataset includes 9291 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054248-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17947146
title: Borderline Personality Disorder Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Borderline Personality Disorder (BPD)

This dataset contains 5,600 synthetic longitudinal records representing 800 patients with Borderline Personality Disorder (ICD-10: F60.3), with comprehensive DBT treatment engagement tracking.

Key Features:



39 clinical variables including all 9 DSM-5 BPD criteria

ZAN-BPD and BSL-23 validated assessment scales

DBT module completion tracking (mindfulness, distress tolerance, emotion regulation, interpersonal effectiveness)

Self-harm patterns and crisis utilization

Privacy-preserving: k-anonymity ≥12, differential privacy ε<1.0


Generated by MentalData.io using clinically-validated distributions. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17941506
title: Borderline Personality Disorder Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Borderline Personality Disorder (BPD)

This dataset contains 5,600 synthetic longitudinal records representing 800 patients with Borderline Personality Disorder (ICD-10: F60.3), with comprehensive DBT treatment engagement tracking.

Key Features:



39 clinical variables including all 9 DSM-5 BPD criteria

ZAN-BPD and BSL-23 validated assessment scales

DBT module completion tracking (mindfulness, distress tolerance, emotion regulation, interpersonal effectiveness)

Self-harm patterns and crisis utilization

Privacy-preserving: k-anonymity ≥12, differential privacy ε<1.0


Generated by MentalData.io using clinically-validated distributions. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17941512
title: Complex PTSD with Dissociation Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Complex PTSD with Dissociative Subtype

This dataset contains 7,800 synthetic longitudinal records representing 600 patients with Complex PTSD (ICD-11: 6B41), with enriched dissociative subtype representation (43.5%).

Key Features:



40 clinical variables including PCL-5, DES-II, CTQ, ITQ scores

ICD-11 CPTSD criteria: re-experiencing, avoidance, hyperarousal, affect dysregulation, negative self-concept, disturbed relationships

Dissociative subtype with depersonalization/derealization tracking

Trauma-informed treatment trajectories

Privacy-preserving: k-anonymity ≥12, differential privacy ε<1.0


Generated by MentalData.io using clinically-validated distributions. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17947147
title: Complex PTSD with Dissociation Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Complex PTSD with Dissociative Subtype

This dataset contains 7,800 synthetic longitudinal records representing 600 patients with Complex PTSD (ICD-11: 6B41), with enriched dissociative subtype representation (43.5%).

Key Features:



40 clinical variables including PCL-5, DES-II, CTQ, ITQ scores

ICD-11 CPTSD criteria: re-experiencing, avoidance, hyperarousal, affect dysregulation, negative self-concept, disturbed relationships

Dissociative subtype with depersonalization/derealization tracking

Trauma-informed treatment trajectories

Privacy-preserving: k-anonymity ≥12, differential privacy ε<1.0


Generated by MentalData.io using clinically-validated distributions. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17945242
title: Dissociative Identity Disorder Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Dissociative Identity Disorder (DID)

Comprehensive synthetic dataset modeling DID (ICD-10: F44.81) with alter system dynamics, switching patterns, and clinical measures. Designed for trauma-informed research.

Key Features:



Multi-table structure: participants, alters, daily logs, clinical measures, EMA responses

Alter system characteristics and inter-alter relationships

DES-II (Dissociative Experiences Scale) tracking

Trauma history and treatment response patterns

Privacy-preserving: designed with k-anonymity and differential privacy


Generated by MentalData.io for ethical DID research. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17947148
title: Dissociative Identity Disorder Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Dissociative Identity Disorder (DID)

Comprehensive synthetic dataset modeling DID (ICD-10: F44.81) with alter system dynamics, switching patterns, and clinical measures. Designed for trauma-informed research.

Key Features:



Multi-table structure: participants, alters, daily logs, clinical measures, EMA responses

Alter system characteristics and inter-alter relationships

DES-II (Dissociative Experiences Scale) tracking

Trauma history and treatment response patterns

Privacy-preserving: designed with k-anonymity and differential privacy


Generated by MentalData.io for ethical DID research. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17945250
title: Anti-NMDAR Encephalitis Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Anti-NMDA Receptor Encephalitis

Synthetic dataset modeling anti-NMDAR encephalitis, a rare autoimmune disorder often misdiagnosed as primary psychiatric illness. Includes confirmed, probable, and misdiagnosed case trajectories.

Key Features:



Multi-stage case classification: confirmed, probable, misdiagnosed

Biomarker evolution (CSF antibody titers, MRI findings)

Symptom trajectories from prodrome to recovery

Treatment response patterns (immunotherapy, tumor resection)

Diagnostic delay modeling for ML-based early detection research


Generated by MentalData.io for rare neuropsychiatric disorder research. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17947153
title: Anti-NMDAR Encephalitis Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Anti-NMDA Receptor Encephalitis

Synthetic dataset modeling anti-NMDAR encephalitis, a rare autoimmune disorder often misdiagnosed as primary psychiatric illness. Includes confirmed, probable, and misdiagnosed case trajectories.

Key Features:



Multi-stage case classification: confirmed, probable, misdiagnosed

Biomarker evolution (CSF antibody titers, MRI findings)

Symptom trajectories from prodrome to recovery

Treatment response patterns (immunotherapy, tumor resection)

Diagnostic delay modeling for ML-based early detection research


Generated by MentalData.io for rare neuropsychiatric disorder research. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17945252
title: Ethically Sensitive Research Phenomena Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Ethically Sensitive Mental Health Phenomena

Synthetic dataset enabling research on rare, ethically complex phenomena that are difficult to study with real patient data due to consent, stigma, and safety concerns.

Key Features:



Factitious disorder / Munchausen presentations

Medical child abuse patterns (for forensic training)

True altruistic vs attention-seeking behavior differentiation

Ambiguous case presentations for diagnostic training

Designed for forensic psychiatry, child protection, and clinical education


Generated by MentalData.io for sensitive research domains. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17947154
title: Ethically Sensitive Research Phenomena Synthetic Dataset v1.0

description: Research-Grade Synthetic Dataset for Ethically Sensitive Mental Health Phenomena

Synthetic dataset enabling research on rare, ethically complex phenomena that are difficult to study with real patient data due to consent, stigma, and safety concerns.

Key Features:



Factitious disorder / Munchausen presentations

Medical child abuse patterns (for forensic training)

True altruistic vs attention-seeking behavior differentiation

Ambiguous case presentations for diagnostic training

Designed for forensic psychiatry, child protection, and clinical education


Generated by MentalData.io for sensitive research domains. No real patient data.

License: CC BY-NC 4.0
DOI: 10.5281/zenodo.17934571
title: SEM Withered Plant (BSE, 2025-12-15 · Set02) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2025-12-15 · Set02) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2025-12-15 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 15 kV with a label magnification of 1000×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.5281/zenodo.17934572
title: SEM Withered Plant (BSE, 2025-12-15 · Set02) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2025-12-15 · Set02) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2025-12-15 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 15 kV with a label magnification of 1000×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.5281/zenodo.17947091
title: SEM Withered Plant (BSE, 2025-12-16 · Set02) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2025-12-16 · Set02) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2025-12-16 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 15 kV with a label magnification of 50×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.5281/zenodo.17947092
title: SEM Withered Plant (BSE, 2025-12-16 · Set02) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2025-12-16 · Set02) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2025-12-16 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 15 kV with a label magnification of 50×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.15468/dl.wwfpcx
title: Occurrence Download

description: A dataset containing 182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Cervus nippon"
 ]
}

 The dataset includes 182 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054250-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25909/4635121
title: The importance of Spatiotemporal Variability in irrigation inputs for hydrological modelling of irrigated catchments - Datasets

description: <b>Data Description</b>Input data used by McInerney et al (2018) for SWAT model calibration for the three different irrigation schedule models:Spatially uniform, continuous (SU_C)Spatially uniform, event-based (SU_EB)Spatially variable, event-based (SV_EB)<br><b>Abstract from McInerney et al (2018)</b>Irrigation contributes substantially to the water balance and environmental condition of many agriculturally productive catchments. This study focuses on the representation of spatio‐temporal variability of irrigation depths in irrigation schedule models. Irrigation variability arises due to differences in farmers' irrigation practices, yet its effects on distributed hydrological predictions used to inform management decisions are currently poorly understood. Using a case study of the Barr Creek catchment in the Murray Darling Basin, Australia, we systematically compare four irrigation schedule models, including uniform vs variable in space, and continuous‐time vs event‐based representations. We evaluate simulated irrigation at hydrological response unit and catchment scales, and demonstrate the impact of irrigation schedules on the simulations of streamflow, evapotranspiration and potential recharge obtained using the Soil and Water Assessment Tool (SWAT). A new spatially‐variable event‐based irrigation schedule model is developed. When used to provide irrigation inputs to SWAT, this new model: (i) reduces the over‐estimation of actual evapotranspiration that occurs with spatially‐uniform continuous‐time irrigation assumptions (biases reduced from ∼40% to ∼2%) and (ii) better reproduces the fast streamflow response to rainfall events compared to spatially‐uniform event‐based irrigation assumptions (seasonally‐adjusted Nash‐Sutcliffe Efficiency improves from 0.15 to 0.56). The stochastic nature of the new model allows representing irrigation schedule uncertainty, which improves the characterization of uncertainty in simulated catchment streamflow and can be used for uncertainty decomposition. More generally, this study highlights the importance of spatio‐temporal variability of inputs to distributed hydrological models and the importance of using multi‐variate response data to test and refine environmental models.<br><br><b>Reference</b>McInerney, D. , Thyer, M. , Kavetski, D. , Githui, F. , Thayalakumaran, T. , Liu, M. and Kuczera, G. (2018), The Importance of Spatio‐Temporal Variability in Irrigation Inputs for Hydrological Modelling of Irrigated Catchments. Water Resour. Res.. <br>
DOI: 10.6084/m9.figshare.5047774
title: Spencer Gulf cumulative impact assessment spatial layers

description: This file set includes:<br><b>Six raster datasets of the processed cumulative impact maps</b> produced for the Spencer Gulf cumulative impact assessment. There are three rasters for the benthic ecosystems and three for the pelagic ecosystem, based on three scoring scenarios (best-case, worst-case and most-likely). The scores came from expert elicitation. For each of the six rasters there is an associated projection file with the same name.<br><b>Six tiff files</b> of the cumulative impact layers (illustrating what they look like when plotted) <br><b>A metadata text file</b> with details of the data layers and the projection - as well as sources of further information.<br><br>
DOI: 10.6084/m9.figshare.5047774.v1
title: Spencer Gulf cumulative impact assessment spatial layers

description: This file set includes:<br><b>Six raster datasets of the processed cumulative impact maps</b> produced for the Spencer Gulf cumulative impact assessment. There are three rasters for the benthic ecosystems and three for the pelagic ecosystem, based on three scoring scenarios (best-case, worst-case and most-likely). The scores came from expert elicitation. For each of the six rasters there is an associated projection file with the same name.<br><b>Six tiff files</b> of the cumulative impact layers (illustrating what they look like when plotted) <br><b>A metadata text file</b> with details of the data layers and the projection - as well as sources of further information.<br><br>
DOI: 10.6084/m9.figshare.5047786
title: Spencer Gulf threat activity spatial layers

description: This file set includes:<br><b>32 raster spatial data layers </b>(scaled to be between 0 and 1) for 32 human activities that were considered as potential threats to marine ecosystems in a cumulative impact assessment of Spencer Gulf in South Australia. The treats are mostly human activities (such as fishing and coastal development), but also include climate change. Each raster data layer has an associated projection file with the same filename. <br><b>32 tiff images</b> showing plots of each threat mapped across the Spencer Gulf study area (illustrating what the data look like when plotted).<br><br><b>A metadata excel file </b>with details about each threat layer, including data sources and native resolution, as well as details on abbreviations used.<br><b>A metadata (read me) text file</b> with information about the spatial data files and the projections - as well as sources of further information.<br>
DOI: 10.6084/m9.figshare.5047786.v1
title: Spencer Gulf threat activity spatial layers

description: This file set includes:<br><b>32 raster spatial data layers </b>(scaled to be between 0 and 1) for 32 human activities that were considered as potential threats to marine ecosystems in a cumulative impact assessment of Spencer Gulf in South Australia. The treats are mostly human activities (such as fishing and coastal development), but also include climate change. Each raster data layer has an associated projection file with the same filename. <br><b>32 tiff images</b> showing plots of each threat mapped across the Spencer Gulf study area (illustrating what the data look like when plotted).<br><br><b>A metadata excel file </b>with details about each threat layer, including data sources and native resolution, as well as details on abbreviations used.<br><b>A metadata (read me) text file</b> with information about the spatial data files and the projections - as well as sources of further information.<br>