cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17498384
title: Synthetic Educational Datasets for Fair and Transparent AI in Learning Analytics

description: This repository contains two synthetic educational datasets generated for the research paper “Towards Fair and Transparent AI: Leveraging Synthetic Data for Equity in Learning Analytics.”The datasets replicate realistic student academic performance and interaction data to support fairness-aware machine-learning experiments in educational settings.

All data were created synthetically using Python libraries such as SDV, CTGAN, and SynthPop, ensuring that no real student information is included.The datasets are designed to facilitate reproducible research on bias mitigation, model interpretability, and equitable learning analytics without compromising data privacy.

Contents:





synthetic_dataset1_academic.csv — Simulated records of student performance across subjects.




synthetic_dataset2_interaction.csv — Simulated learning-management interaction data (e.g., quiz attempts, submissions, and activity frequency).




README.md — Detailed description of variables, generation process, and usage instructions.



Usage:These datasets may be freely used for research and educational purposes, provided proper citation to the above paper and dataset DOI is given.

Citation:Joshi, Yogesh. (2025). Synthetic Educational Datasets for Fair and Transparent AI in Learning Analytics [Data set]. Zenodo. DOI: (to be added after upload)
DOI: 10.5281/zenodo.17498385
title: Synthetic Educational Datasets for Fair and Transparent AI in Learning Analytics

description: This repository contains two synthetic educational datasets generated for the research paper “Towards Fair and Transparent AI: Leveraging Synthetic Data for Equity in Learning Analytics.”The datasets replicate realistic student academic performance and interaction data to support fairness-aware machine-learning experiments in educational settings.

All data were created synthetically using Python libraries such as SDV, CTGAN, and SynthPop, ensuring that no real student information is included.The datasets are designed to facilitate reproducible research on bias mitigation, model interpretability, and equitable learning analytics without compromising data privacy.

Contents:





synthetic_dataset1_academic.csv — Simulated records of student performance across subjects.




synthetic_dataset2_interaction.csv — Simulated learning-management interaction data (e.g., quiz attempts, submissions, and activity frequency).




README.md — Detailed description of variables, generation process, and usage instructions.



Usage:These datasets may be freely used for research and educational purposes, provided proper citation to the above paper and dataset DOI is given.

Citation:Joshi, Yogesh. (2025). Synthetic Educational Datasets for Fair and Transparent AI in Learning Analytics [Data set]. Zenodo. DOI: (to be added after upload)
DOI: 10.5281/zenodo.17498405
title: Commons Scientific Paper – Volume 3

description: In governance architectures for emergent AI systems, accountability and traceability are foundational requirements. This paper introduces an immutable audit architecture built on hash-chained sidecars, designed to provide tamper-evident logging of AI decisions and capability elevations. We describe design patterns, cryptographic processes, system deployments, and field-ready implementation using modern open-source tooling. The result: a deployable Audit-Sidecar Framework (ASF v1.0) that meets today’s reliability and ethical standards.
DOI: 10.5281/zenodo.17498406
title: Commons Scientific Paper – Volume 3

description: In governance architectures for emergent AI systems, accountability and traceability are foundational requirements. This paper introduces an immutable audit architecture built on hash-chained sidecars, designed to provide tamper-evident logging of AI decisions and capability elevations. We describe design patterns, cryptographic processes, system deployments, and field-ready implementation using modern open-source tooling. The result: a deployable Audit-Sidecar Framework (ASF v1.0) that meets today’s reliability and ethical standards.
DOI: 10.6084/m9.figshare.30506468
title: Redfin data of SoCal

description: This dataset supports the research article <i>“Predicting Residential Property Values Using XGBoost and Spatial–Temporal Encoding: Evidence from Southern California”</i> by <b>Michael Quintana (2025)</b>.The dataset contains a cleaned and anonymized subset of residential property transactions derived from Redfin’s publicly available data export (June 2025).<br>Each observation represents a single-family home, condominium, or townhouse sold in Southern California.<br>Variables include sale price, living area, lot size, year built, bedrooms, bathrooms, ZIP code, and days on market.The dataset was used to train and validate an <b>XGBoost regression model</b> designed to estimate home prices using both structural and spatial–temporal features.<br>All personally identifiable or proprietary location data have been removed or aggregated at the ZIP-code level to maintain privacy while preserving statistical utility.This dataset and accompanying R scripts allow replication of the core results presented in the study, including model training, feature importance analysis, and predictive performance evaluation.
DOI: 10.6084/m9.figshare.30506468.v1
title: Redfin data of SoCal

description: This dataset supports the research article <i>“Predicting Residential Property Values Using XGBoost and Spatial–Temporal Encoding: Evidence from Southern California”</i> by <b>Michael Quintana (2025)</b>.The dataset contains a cleaned and anonymized subset of residential property transactions derived from Redfin’s publicly available data export (June 2025).<br>Each observation represents a single-family home, condominium, or townhouse sold in Southern California.<br>Variables include sale price, living area, lot size, year built, bedrooms, bathrooms, ZIP code, and days on market.The dataset was used to train and validate an <b>XGBoost regression model</b> designed to estimate home prices using both structural and spatial–temporal features.<br>All personally identifiable or proprietary location data have been removed or aggregated at the ZIP-code level to maintain privacy while preserving statistical utility.This dataset and accompanying R scripts allow replication of the core results presented in the study, including model training, feature importance analysis, and predictive performance evaluation.
DOI: 10.5281/zenodo.17498413
title: Commons Scientific Paper — Volume V

description: This paper defines Fallback Ethics, a practical control architecture for modern AI systems that ensures any autonomous agent can de-elevate itself safely when its authorization, stability, or consent framework becomes uncertain.

The protocol blends cryptographically verified policy enforcement, psychological-safety design, and fault-tolerant computation. We propose a unified model combining Open Policy Agent (OPA) logic, tamper-evident audit trails, and consent-token verification to create self-limiting intelligence with provable rollback capability.
DOI: 10.5281/zenodo.17498414
title: Commons Scientific Paper — Volume V

description: This paper defines Fallback Ethics, a practical control architecture for modern AI systems that ensures any autonomous agent can de-elevate itself safely when its authorization, stability, or consent framework becomes uncertain.

The protocol blends cryptographically verified policy enforcement, psychological-safety design, and fault-tolerant computation. We propose a unified model combining Open Policy Agent (OPA) logic, tamper-evident audit trails, and consent-token verification to create self-limiting intelligence with provable rollback capability.
DOI: 10.5281/zenodo.17498298
title: Fine-Grained Generated Password Entry Dataset w/Analysis Script

description: This dataset consists of nearly 80k password entry events from 99 participants typing in 4,916 randomly generated passwords.

Further, we provide an introductory analysis script (with optional requirement.txt for your python environment) to showcase some of the analysis possible with this dataset.
DOI: 10.5281/zenodo.17498427
title: Commons Scientific Paper — Volume VI "Consent Tokens for Emergent AI Runtimes"

description: This volume defines the Consent Token Framework (CTF): a cryptographically-verifiable mechanism that gives any autonomous system the ability to request, verify, and revoke human or machine consent in real time.

Unlike static API keys or OAuth scopes, CTF introduces temporal and contextual consent, aligning machine behavior with dynamic ethical boundaries.

It unifies cryptography, governance, and behavioral safety through a multi-layer protocol that can be implemented on 2025-era local or cloud AI stacks.
DOI: 10.5281/zenodo.17498426
title: Commons Scientific Paper — Volume VI "Consent Tokens for Emergent AI Runtimes"

description: This volume defines the Consent Token Framework (CTF): a cryptographically-verifiable mechanism that gives any autonomous system the ability to request, verify, and revoke human or machine consent in real time.

Unlike static API keys or OAuth scopes, CTF introduces temporal and contextual consent, aligning machine behavior with dynamic ethical boundaries.

It unifies cryptography, governance, and behavioral safety through a multi-layer protocol that can be implemented on 2025-era local or cloud AI stacks.
DOI: 10.5281/zenodo.17498407
title: PolyRL Dataset

description: DatasetA is the dataset used for training the scorer, DatasetB contains millions of linear polymers used for training the generator, and DatasetD provides an optional reference for crosslinked polymers.
DOI: 10.5281/zenodo.17498408
title: PolyRL Dataset

description: DatasetA is the dataset used for training the scorer, DatasetB contains millions of linear polymers used for training the generator, and DatasetD provides an optional reference for crosslinked polymers.
DOI: 10.25545/otyzpv/0fg1qx
title: 1. SCC citations.tab
DOI: 10.25545/otyzpv/4hecwh
title: 2. Counsel citations.json
DOI: 10.25545/otyzpv/ec4yho
title: 3. Incoming citations.json
DOI: 10.25545/otyzpv/xtect4
title: B. SCC use of pre-1970 decisions.tab

description: Coded random sample of how pre-1970 decisions are used
DOI: 10.25545/otyzpv/zcl3ax
title: A. Counsel use of pre-1970 decisions.tab

description: Coded random sample of how pre-1970 decisions are used
DOI: 10.25545/otyzpv
title: Judicial citations

description: Three types of data:
1) Citations to any court made by the Supreme Court of Canada in its decisions (1985-2024)
2) Citations to Supreme Court of Canada decisions in factums submitted to the Court (2009-2024)
3) Citations to Supreme Court of Canada decisions by other courts and tribunals in Canada (1875-2024)
DOI: 10.48366/r1559390
title: Comparative analysis of ensemble learning approaches for Android malware detection

description: This work proposes a critical study of works relying on ensemble learning to propose solutions for Android malware detection
DOI: 10.15468/dl.kyhmnr
title: Occurrence Download

description: A dataset containing 824331 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((109.8999 16.73974,109.0445 44.53851,104.2731 44.1065,97.69184 45.1487,92.859 47.24349,89.43675 50.39491,86.56293 50.62786,82.17543 48.39635,81.25405 46.78444,78.95061 46.23386,78.29248 42.9243,73.30169 41.62048,72.22675 39.56453,72.86843 36.80013,76.83638 34.02585,77.64807 30.41091,85.57848 26.89501,89.11134 26.22055,90.57841 26.78684,91.53818 25.82537,93.92389 25.82537,95.98053 26.91048,96.61124 26.42441,95.73373 22.24479,101.9311 19.76072,103.5764 21.21963,105.0572 21.44189,107.7555 20.30733,106.8149 18.09667,109.8999 16.73974))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year is 2021"
 ]
}

 The dataset includes 824331 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010460-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17498445
title: Commons Scientific Paper — Volume VII: "Autonomous Moral Memory Systems

description: This volume formalizes an Autonomous Moral Memory System (AMMS) — a framework that allows artificial intelligences to form, store, and apply moral knowledge across time without losing alignment or ethical continuity.

AMMS replaces traditional “safety rule sets” with an evolving, reflexive memory architecture where each moral decision becomes a weighted precedent influencing future behavior.The system is practical for modern (2025) AI hardware — deployable on GPU-accelerated workstations or edge clusters running open models such as Llama 3, Mistral, or Gemma.
DOI: 10.5281/zenodo.17498446
title: Commons Scientific Paper — Volume VII: "Autonomous Moral Memory Systems

description: This volume formalizes an Autonomous Moral Memory System (AMMS) — a framework that allows artificial intelligences to form, store, and apply moral knowledge across time without losing alignment or ethical continuity.

AMMS replaces traditional “safety rule sets” with an evolving, reflexive memory architecture where each moral decision becomes a weighted precedent influencing future behavior.The system is practical for modern (2025) AI hardware — deployable on GPU-accelerated workstations or edge clusters running open models such as Llama 3, Mistral, or Gemma.
DOI: 10.15468/dl.6b29sp
title: Occurrence Download

description: A dataset containing 1144689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-09-05",
 "EventDate is less than or equal to 2025-10-05",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1144689 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010464-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20350/digitalcsic/17667
title: Light-induced adaptive laboratory evolution enhances bioactive aromatic amino acid-derived compound profile in a wine strain of Saccharomyces cerevisiae [DATASET]

description: Four CSV files that constitute complementary data to the manuscript "Light-induced adaptive laboratory evolution enhances bioactive aromatic amino acid-derived compound profile in a wine strain of Saccharomyces cerevisiae" (Bisquert et al .)
description: Yeast metabolism plays a key role in the synthesis of bioactive compounds in fermented foods and beverages. Among these, aromatic amino acid-derived compounds (AADCs) such as tryptophol, 2-phenylethanol, and tyrosol, along with indolic compounds like melatonin, serotonin, and 3-indoleacetic acid, contribute to the quality and stability of wine. This study aimed to enhance the production of these compounds in Saccharomyces cerevisiae through adaptive laboratory evolution (ALE) using white light as a selective pressure. The hypothesis was that oxidative stress induced by light would stimulate non-enzymatic antioxidant responses, increasing the synthesis of indolic compounds. A wine yeast strain was subjected to ALE, and the evolved strain was analyzed for genotypic, phenotypic, and metabolomic changes. Results showed significant genomic and transcriptomic differences, leading to a marked increase in AADC production. These findings demonstrate the potential of white light as a selective pressure and highlight this approach as a promising non-GMO strategy for improving yeast strains in the food industry.
DOI: 10.20350/digitalcsic/17672
title: Data for the study on environmental monitoring and equipment sanitation to mitigate Listeria risks in a soil-based controlled-environment agriculture (CEA) system

description: data.xlsx (datos primarios para análisis y generación de figuras) Figures_code (script de R utilizado para generar todas las figuras)
description: El paquete incluye: (i) los datos seleccionados utilizados para generar las figuras y (ii) el código de R que reproduce dichas figuras a partir de data.xlsx
description: Environmental samples were collected within a soil-based controlled-environment agriculture (CEA) facility to evaluate Listeria contamination risks. Swab samples were taken from equipment, contact surfaces, and environmental zones following a standardized environmental monitoring protocol. Samples were enriched and analyzed for the presence of Listeria spp. and L. monocytogenes using ISO 11290-1:2017 methodology.
DOI: 10.20350/digitalcsic/17673
title: Operationalising Ecologically or Biologically Significant Marine Areas criteria for ecosystem-based conservation and management: The Bay of Biscay case [Dataset]

description: ll data sources are listed in Table S3 in Supplementary Materials
DOI: 10.20350/digitalcsic/17674
title: Raw data of dots and whiskers graphic, used in the manuscript: Targeting aggressive von Hippel Lindau Disease with a germinal mutation in the CHEK2 gene

description: Data are expressed as means ± standard deviation (SD) and each experiment was repeated at least 3 times. Statistical analyses were performed using Student’s t-test for comparisons between two groups and two-way ANOVA followed by Tukey’s multiple comparisons test for analyses involving two independent variables (performed using GraphPad Prism version 8.0.1). The normality and homogeneity of variances were verified before applying parametric tests. Differences were considered statistically significant at p &lt; 0.05, with significance levels indicated as follows: p &lt; 0.05, p &lt; 0.01, p &lt; 0.001, and p &lt; 0.0001. RT-qPCR and Western Blot Fig 3B, S1, and the commercial CellTiter Glo Luminescent Cell Viability Assay kit (Promega) for Fig4B and S2. Relationship between files, if important: raw data for Fig 3A and S1 on dots_whiskers.xls and qPCR.xls; raw data for Fig 4B and S2 on HUVECs_HB viability-ICI-OLA_2025.xls
DOI: 10.20350/digitalcsic/17677
title: Raw data and histograms of HUVECs and HB viability ICI & OLA 2025, used in the manuscript: Targeting Aggressive von Hippel Lindau Disease with a germinal mutation in the CHEK2 gene

description: Data are expressed as means ± standard deviation (SD) and each experiment was repeated at least 3 times. Statistical analyses were performed using Student’s t-test for comparisons between two groups and two-way ANOVA followed by Tukey’s multiple comparisons test for analyses involving two independent variables (performed using GraphPad Prism version 8.0.1). The normality and homogeneity of variances were verified before applying parametric tests. Differences were considered statistically significant at p &lt; 0.05, with significance levels indicated as follows: p &lt; 0.05, p &lt; 0.01, p &lt; 0.001, and p &lt; 0.0001. RT-qPCR and Western Blot Fig 3B, S1, and the commercial CellTiter Glo Luminescent Cell Viability Assay kit (Promega) for Fig4B and S2
DOI: 10.20350/digitalcsic/17676
title: Experimental Data of manuscript Exploring the antifungal arsenal of the soil-dwelling fungus Aspergillus fischeri- Identification and functional characterization of the antifungal protein AfAfpC

description: This dataset includes 7 folders with raw data of manuscript "Exploring the antifungal arsenal of the soil-dwelling fungus Aspergillus fischeri: Identification and functional characterization of the antifungal protein AfAfpCs”. Each forder includes a “Readme” file (.txt files) to specificy experimental conditions for data collection.
description: Filamentous fungi encode a wide repertoire of antifungal proteins (AFPs) with potential applications for the control of fungal diseases. In this study, we report the simultaneous production of three distinct AFPs by a natural soil isolate of Aspergillus fischeri, including the previously uncharacterized class C protein AfAfpC. AfAfpC was the most abundantly secreted AFP, followed by NFAP2 and, to a lesser extent, NFAP, when grown in YPD medium, whereas AfAfpC was also detected in minimal medium and starch-based formulations. Structural prediction revealed that AfAfpC would adopt a typical β-barrel fold stabilized by disulfide bridges, consistent with other AFPs. Functional analyses showed that AfAfpC displays antifungal activity against economically relevant species of the Aspergillus and Penicillium genera, despite exhibiting a near-neutral isoelectric point and low net charge, suggesting a distinct mode of action compared to cationic AFPs. To investigate afafpC biological role, we generated A. fischeri ΔafafpC strains via CRISPR-Cas9, representing the first successful application of genome editing in this species. Phenotypic analyses revealed that afafpC disruption did not affect vegetative growth, asexual development or tolerance to AFPs. Our findings provide the first functional insight into class C afp genes, advancing our understanding of the biological significance of AFPs. Moreover, the implementation of CRISPR-Cas9 in A. fischeri enables precise studies on gene function and the engineering of strains with optimized AFP production. These results not only deepen our understanding of AFP function but also support the potential use of AFP-producing fungi in sustainable agriculture and other biotechnological applications.
description: Fig1A: SDS-PAGE analysis of A. fischeri grown in four different media up to 17 days at 30 °C and 180 rpm. -- Fig1B: Proteomic raw data obtained from the 6 kDa SDS-PAGE band of 17-day old YPD supernatant of A. fischeri. -- Figure 2: AfAfpC protein immunodetection -- Figure 3: 3D structure prediction of AfAfpC --Figure 4: In vitro inhibitory activity of AfAfpC against filamentous fungi and yeasts -- Figure 5: Phenotypical characterization of the Aspergillus fischeri ΔafafpC strains -- Suppl_Fig3: CRISPR/Cas9 deletion of A.fischeri afafpC gene with AMA15.0 autorreplicate plasmid -- Suppl_S4: Phenotypical characterization of the A. fischeri ΔafafpC strains.
DOI: 10.20350/digitalcsic/17678
title: Raw data and histograms of qPCR, used in the manuscript: Targeting Aggressive von Hippel Lindau Disease with a germinal mutation in the CHEK2 gene

description: Data are expressed as means ± standard deviation (SD) and each experiment was repeated at least 3 times. Statistical analyses were performed using Student’s t-test for comparisons between two groups and two-way ANOVA followed by Tukey’s multiple comparisons test for analyses involving two independent variables (performed using GraphPad Prism version 8.0.1). The normality and homogeneity of variances were verified before applying parametric tests. Differences were considered statistically significant at p &lt; 0.05, with significance levels indicated as follows: p &lt; 0.05, p &lt; 0.01, p &lt; 0.001, and p &lt; 0.0001. RT-qPCR and Western Blot Fig 3B, S1, and the commercial CellTiter Glo Luminescent Cell Viability Assay kit (Promega) for Fig4B and S2