cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13169823
title: Binary black-hole simulation SXS:BBH:0376

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169824
title: Binary black-hole simulation SXS:BBH:0376

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.7j2wnz
title: Occurrence Download

description: A dataset containing 8608 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "HasGeospatialIssue is false",
 "ScientificName is one of (Arion circumscriptus, Arion circumscriptus G.Johnston, 1828, Arion silvaticus, Arion silvaticus Lohmander, 1937, Crocus biflorus, Crocus biflorus Mill., Filipendula camschatica, Filipendula camschatica (Pall.) Maxim.)"
 ]
}

 The dataset includes 8608 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045664-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13161575
title: Raw data and detailed descriptions
DOI: 10.5281/zenodo.13169830
title: Binary black-hole simulation SXS:BBH:0347

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169831
title: Binary black-hole simulation SXS:BBH:0347

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.twc2zx
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bees of the Melittidae family of the Kemerovo region",
 "TaxonKey is Melitta Kirby, 1802"
 ]
}

 The dataset includes 176 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045665-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13161576
title: Raw data and detailed descriptions
DOI: 10.5281/zenodo.13169836
title: Binary black-hole simulation SXS:BBH:0469

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169838
title: Binary black-hole simulation SXS:BBH:0469

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13141761
title: Ccc

description: c
DOI: 10.5281/zenodo.13169850
title: Binary black-hole simulation SXS:BBH:0353

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169851
title: Binary black-hole simulation SXS:BBH:0353

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169854
title: Binary black-hole simulation SXS:BBH:0440

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13169855
title: Binary black-hole simulation SXS:BBH:0440

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.21227/vd24-na59
title: Sparse Combinatorial Dataset

description: Recently, combinatorial interaction strategies have a large spectrum as black box strategies for testing software and hardware. This paper discusses a novel adoption of a combinatorial interaction strategy to generate a sparse combinatorial data table (SCDT) for machine learning. Unlike test data generation strategies, in which the t-way tuples synthesize into a test case, the proposed SCDT requires analyzing instances against their corresponding tuples to generate a systematic learning dataset. This paper reviews the state-of-the-art literature on machine learning and recommends the WEKA workbench tool to help with the experiments. Four discrete datasets, namely: Weather, Lung Cancer, Autistic Spectrum Disorder Screening Data (ADS) for Children, and Caesarian, were evaluated and compared with the related works. In addition, extensive experiments illustrated the derivation of SCDT, the selection of classifiers, the learning of classifiers, the evaluation of classifiers, and the discussion of lessons learned from experiences. Five algorithms (A1DE, A2DE, IBk, kStar, and Random Tree) out of twenty-seven are suitable for SCDT training. The practical results show that these five algorithms, when trained with 2-way SCDT for the Weather dataset, yield ideal accuracy (100%). Besides that, ideal accuracy is obtained by: A1DE, A2DE, and IBk algorithms when trained with 2-way SCDT for the Lung cancer dataset; A2DE and IBk algorithms when trained with 5-way SCDT for the ADS dataset; the IBk and kStar algorithms when trained with 4-way SCDT for the Caesarian dataset. Moreover, the accuracy obtained by adopting the SCDT training is promising as compared to related works and the conventional random selection of training instances. Furthermore, the SCDT assists in detecting and reporting conflicts in the dataset before the training phase. Finally, this paper states the contribution of the proposed SCDT and future research prospects in this field.
DOI: 10.15468/dl.zk2j8p
title: Occurrence Download

description: A dataset containing 587890 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Populus tremula L."
 ]
}

 The dataset includes 587890 records from 2430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045653-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17181580
title: MS Thesis Study 1 Dataset
DOI: 10.5281/zenodo.17181579
title: MS Thesis Study 1 Dataset
DOI: 10.6084/m9.figshare.30185830
title: <b>External attribution to economic inequality increases algorithm preference</b>

description: Five studies comprising seven experiments (N = 961)showed that attributing inequality to external factors increased individuals’preference for algorithmic decisions. This effect was observed across multiple contexts (Study 1A: healthcare; Study 1B: recruitment; Study 1C: education) and diverse methods (Study 2), and was driven by the psychological mechanism of decreased system-justifying beliefs (Studies 3 and 4). Furthermore, Study 5 explored the social consequences of this effect, showing that externally attributing individuals perceived higher fairness from algorithmic decisions in unequal contexts, whereas internally attributing individuals perceived reduced fairness. These findings indicate that the effectiveness of algorithms in mitigating inequalitydepends on contextual factors and individual cognitive styles, rather thanremaining universally applicable. Future research should attend to potential backfire effects of algorithmic interventions, particularly the risk of undermining individuals’perceptions of fairness in unequal contexts.
DOI: 10.6084/m9.figshare.30185830.v1
title: <b>External attribution to economic inequality increases algorithm preference</b>

description: Five studies comprising seven experiments (N = 961)showed that attributing inequality to external factors increased individuals’preference for algorithmic decisions. This effect was observed across multiple contexts (Study 1A: healthcare; Study 1B: recruitment; Study 1C: education) and diverse methods (Study 2), and was driven by the psychological mechanism of decreased system-justifying beliefs (Studies 3 and 4). Furthermore, Study 5 explored the social consequences of this effect, showing that externally attributing individuals perceived higher fairness from algorithmic decisions in unequal contexts, whereas internally attributing individuals perceived reduced fairness. These findings indicate that the effectiveness of algorithms in mitigating inequalitydepends on contextual factors and individual cognitive styles, rather thanremaining universally applicable. Future research should attend to potential backfire effects of algorithmic interventions, particularly the risk of undermining individuals’perceptions of fairness in unequal contexts.
DOI: 10.15468/dl.y7nq99
title: Occurrence Download

description: A dataset containing 80578 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "HasCoordinate is true",
 "Year is 1955"
 ]
}

 The dataset includes 80578 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007702-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26186/150573
title: DEA Water Observations Statistics (Landsat) Version 2.0.0

description: These are the statistics generated from the DEA Water Observations (Water Observations from Space) suite of products, which gives summaries of how often surface water was observed by the Landsat satellites for various periods (per year, per season and for the period from 1986 to 2023).<br>The spatial extent is all Australia and the spatial resolution is 30 m x 30 m.<br>DEA Water Observations Statistics is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available:<strong>- DEA WO Multi-Year:</strong> ga_ls_wo_fq_myear_3: statistics calculated from the full depth of time series (1986 to 2023) unfiltered<strong>- DEA WO Calendar Year:</strong> ga_ls_wo_fq_cyear_3: statistics calculated from each calendar year (1986 to present)<strong>- DEA WO November to March:</strong> ga_ls_wo_fq_nov_mar_3: statistics calculated yearly from November to March (1986 to present)<strong>- DEA WO April to October:</strong> ga_ls_wo_fq_apr_oct_3: statistics calculated yearly from April to October (1986 to present)<br>Each dataset in this product contains the following information:- Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems)- Wet Count: how many times water was detected in observations that were clear- Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage)<br>As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations Filtered Statistics product, which provides a noise-reduced view of the all-of-time water summary.<br>Band names:- <strong>count_wet: </strong>how many times water was detected in observations that were clear.- <strong>count_clear: </strong> how many times an area could be clearly seen.- <strong>frequency: </strong>what ratio of clear observations were detected as wet (represented as a number between 0 and 1 corresponding to 0 to 100% of observations)
DOI: 10.26186/150572
title: DEA Tasseled Cap Percentiles (Landsat) Version 2.0.0

description: This product offers three percentiles per Tasseled Cap index per calendar year. The percentiles are 10th, 50th, and 90th; the Tasseled Cap indexes are ‘greenness’, ‘wetness’, and ‘brightness’; and the years are 1987 to the latest full calendar year.It includes cloud and shadow buffering with a size of 6 pixels. This buffering is applied to Landsat 5, Landsat 7, Landsat 8, and Landsat 9 data from 2022 onwards.<br>The spatial extent is all Australia and the spatial resolution is 30 m x 30 m.<br>Link to data: https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_tc_pc_cyear_3/2-0-0/Link to DEA Knowledge Hub: https://knowledge.dea.ga.gov.au/data/product/dea-tasseled-cap-percentiles-landsat/<br><br>
DOI: 10.26186/150571
title: DEA Mangroves (Landsat) Version 4.0.0

description: Digital Earth Australia (DEA) Mangroves classifies mangrove canopy cover density across Australia’s coastal ecosystems using 35+ years of Landsat data (1987–present) at 30-meter resolution. <br>Each annual map contains one layer (band) canopy_cover_class, with values defined as follows:<strong>Not observed</strong> — 0: insufficient observations per year to determine if mangroves exist<strong>Woodland 20-50% </strong>— 1: mangroves present with Planimetric Canopy Cover Percentage (PCC%) between 20% and 50%<strong>Open forest 50-80% </strong>— 2: mangroves present with PCC% between 50% and 80%<strong>Closed forest 80-100% </strong> — 3: mangroves present with PCC% above 80%<strong>No data </strong> — 255: no mangroves present, either PCC% below 20% or outside mangrove habitat<br><br>Link to data: https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_mangrove_cover_cyear_3/4-0-0/Link to DEA Knowledge Hub: https://knowledge.dea.ga.gov.au/data/product/dea-mangroves/
DOI: 10.6084/m9.figshare.30185797
title: Health Expenditures, Life Expectancy and Infant Mortality in Nigeria (1990-2023).csv

description: This dataset supports a study of how disaggregated health-financing segments viz. health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending, relate to social sustainability in Nigeria, measured by life expectancy at birth and infant mortality rate. By separating recurrent vs capital government spending and household out-of-pocket payments from per-capita spending, the data enable clean tests of financing composition effects on population health.<b>Variables (what each column means)/source sites</b><b>YEAR</b>: Calendar year.<b>LEX_years: </b>Life expectancy at birth, total (years).<br><b>Source site:</b> World Bank WDI → https://data.worldbank.org<b>IMR_per_1000: </b>Infant mortality rate (deaths under age 1 per 1,000 live births).<br><b>Source site:</b> World Bank WDI → https://data.worldbank.org<b>HEXPc_USD: </b>Current health expenditure per capita (current US$).<br><b>Source site:</b> World Bank WDI (from WHO GHED) → https://data.worldbank.org<b>OPHS_percent: </b>Out-of-pocket spending as a % of current health expenditure (households’ direct payments).<br><b>Source site:</b> World Bank WDI / WHO Global Health Expenditure Database → https://data.worldbank.org ; https://apps.who.int/nha/database<b>RHEX_Naira_billion: </b>Federal Government Recurrent health expenditure (₦ billions).<br><b>Source site:</b> CBN Statistical Bulletin → https://www.cbn.gov.ng (Statistics ▸ Statistical Bulletin ▸ Public Finance)<b>CHEX_Naira_billion:</b> Federal Government Capital health expenditure (₦ billions).<br><b>Source site:</b> CBN Statistical Bulletin → https://www.cbn.gov.ng (Statistics ▸ Statistical Bulletin ▸ Public Finance)<b><i>Notes: </i></b><i>Spending series from WDI/WHO typically have strongest coverage from 2000 onward; earlier years may be NA. RHEX/CHEX are nominal ₦ billions from CBN Public Finance functional tables (Health).</i>
DOI: 10.6084/m9.figshare.30185797.v1
title: Health Expenditures, Life Expectancy and Infant Mortality in Nigeria (1990-2023).csv

description: This dataset supports a study of how disaggregated health-financing segments viz. health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending, relate to social sustainability in Nigeria, measured by life expectancy at birth and infant mortality rate. By separating recurrent vs capital government spending and household out-of-pocket payments from per-capita spending, the data enable clean tests of financing composition effects on population health.<b>Variables (what each column means)/source sites</b><b>YEAR</b>: Calendar year.<b>LEX_years: </b>Life expectancy at birth, total (years).<br><b>Source site:</b> World Bank WDI → https://data.worldbank.org<b>IMR_per_1000: </b>Infant mortality rate (deaths under age 1 per 1,000 live births).<br><b>Source site:</b> World Bank WDI → https://data.worldbank.org<b>HEXPc_USD: </b>Current health expenditure per capita (current US$).<br><b>Source site:</b> World Bank WDI (from WHO GHED) → https://data.worldbank.org<b>OPHS_percent: </b>Out-of-pocket spending as a % of current health expenditure (households’ direct payments).<br><b>Source site:</b> World Bank WDI / WHO Global Health Expenditure Database → https://data.worldbank.org ; https://apps.who.int/nha/database<b>RHEX_Naira_billion: </b>Federal Government Recurrent health expenditure (₦ billions).<br><b>Source site:</b> CBN Statistical Bulletin → https://www.cbn.gov.ng (Statistics ▸ Statistical Bulletin ▸ Public Finance)<b>CHEX_Naira_billion:</b> Federal Government Capital health expenditure (₦ billions).<br><b>Source site:</b> CBN Statistical Bulletin → https://www.cbn.gov.ng (Statistics ▸ Statistical Bulletin ▸ Public Finance)<b><i>Notes: </i></b><i>Spending series from WDI/WHO typically have strongest coverage from 2000 onward; earlier years may be NA. RHEX/CHEX are nominal ₦ billions from CBN Public Finance functional tables (Health).</i>
DOI: 10.5281/zenodo.17181591
title: A Unified Framework for Soft Robotics (2025–2040): Integrating Materials, Modeling, AI-Driven Control, Fabrication Pipelines, and Global Ethical Governance

description: 📌 Title

 

A Unified Framework for Soft Robotics (2025–2040): Integrating Materials, Modeling, AI-Driven Control, Fabrication Pipelines, and Global Ethical Governance

 

 

---

 

📌 Description / Abstract (for Zenodo entry)

 

This research presents a comprehensive and interdisciplinary framework for advancing soft robotics from fragmented laboratory prototypes toward globally scalable, reproducible, and ethically governed technologies. Bridging materials science, continuum modeling, artificial intelligence, fabrication engineering, and policy foresight, the study proposes a unified pipeline that directly addresses reproducibility deficits, scalability bottlenecks, and regulatory blind spots in the field.

 

The framework integrates standardized benchmarking of elastomers, hydrogels, dielectric actuators, biodegradable composites, and shape-memory systems; advanced constitutive and continuum models (Kelvin–Voigt, Ogden, Cosserat rods) coupled with digital twins and uncertainty quantification; hybrid AI–physics control architectures combining model predictive control (MPC) and reinforcement learning (RL); and scalable fabrication pipelines encompassing mold casting, lithography, DIW printing, and hybrid manufacturing. Validation protocols employ ANOVA, Weibull reliability analysis, power statistics, and pre-registered open datasets, ensuring transparent reproducibility. Ethical and governance dimensions align with FDA, EU MDR, ISO standards, and the UN Sustainable Development Goals, while proposing an International Soft Robotics Governance Council (ISRGC).

 

The roadmap (2025–2040) defines three phases—Consolidation, Expansion, and Maturity—marking the transition of soft robotics into medicine, sustainable environmental systems, and adaptive industrial automation. By merging technical innovation with normative responsibility, this work positions soft robotics as a 21st-century socio-technological pillar for safe, inclusive, and sustainable human progress.

 

 

---

 

📌 Author & Affiliation

 

Author:

B. Mazumdar, Scholar-at-Large

Independent Researcher, Ausara, West Bengal, India

Email: bumbamazumdar2017@gmail.com

ORCID: https://orcid.org/0009-0007-5615-3558

 

Affiliation (for Zenodo metadata):

Independent Researcher, India

 

 

---

 

📌 Keywords / Subjects

 

Soft Robotics

 

Viscoelastic Modeling

 

Hybrid AI–Physics Control

 

Digital Twin Validation

 

Fabrication Pipelines

 

Uncertainty Quantification

 

Ethical AI & Global Governance

 

Sustainable Robotics Roadmap (2025–2040)
DOI: 10.5281/zenodo.17181592
title: A Unified Framework for Soft Robotics (2025–2040): Integrating Materials, Modeling, AI-Driven Control, Fabrication Pipelines, and Global Ethical Governance

description: 📌 Title

 

A Unified Framework for Soft Robotics (2025–2040): Integrating Materials, Modeling, AI-Driven Control, Fabrication Pipelines, and Global Ethical Governance

 

 

---

 

📌 Description / Abstract (for Zenodo entry)

 

This research presents a comprehensive and interdisciplinary framework for advancing soft robotics from fragmented laboratory prototypes toward globally scalable, reproducible, and ethically governed technologies. Bridging materials science, continuum modeling, artificial intelligence, fabrication engineering, and policy foresight, the study proposes a unified pipeline that directly addresses reproducibility deficits, scalability bottlenecks, and regulatory blind spots in the field.

 

The framework integrates standardized benchmarking of elastomers, hydrogels, dielectric actuators, biodegradable composites, and shape-memory systems; advanced constitutive and continuum models (Kelvin–Voigt, Ogden, Cosserat rods) coupled with digital twins and uncertainty quantification; hybrid AI–physics control architectures combining model predictive control (MPC) and reinforcement learning (RL); and scalable fabrication pipelines encompassing mold casting, lithography, DIW printing, and hybrid manufacturing. Validation protocols employ ANOVA, Weibull reliability analysis, power statistics, and pre-registered open datasets, ensuring transparent reproducibility. Ethical and governance dimensions align with FDA, EU MDR, ISO standards, and the UN Sustainable Development Goals, while proposing an International Soft Robotics Governance Council (ISRGC).

 

The roadmap (2025–2040) defines three phases—Consolidation, Expansion, and Maturity—marking the transition of soft robotics into medicine, sustainable environmental systems, and adaptive industrial automation. By merging technical innovation with normative responsibility, this work positions soft robotics as a 21st-century socio-technological pillar for safe, inclusive, and sustainable human progress.

 

 

---

 

📌 Author & Affiliation

 

Author:

B. Mazumdar, Scholar-at-Large

Independent Researcher, Ausara, West Bengal, India

Email: bumbamazumdar2017@gmail.com

ORCID: https://orcid.org/0009-0007-5615-3558

 

Affiliation (for Zenodo metadata):

Independent Researcher, India

 

 

---

 

📌 Keywords / Subjects

 

Soft Robotics

 

Viscoelastic Modeling

 

Hybrid AI–Physics Control

 

Digital Twin Validation

 

Fabrication Pipelines

 

Uncertainty Quantification

 

Ethical AI & Global Governance

 

Sustainable Robotics Roadmap (2025–2040)