cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15230565
title: SQuASH Surrogate Benchmark Dataset for Quantum Architecture Search (QAS)

description: This dataset supports the SQuASH benchmark for Quantum Architecture Search (QAS), as presented in our paper. It includes training and evaluation data used for surrogate model learning, structured into multiple problem instances. Each subdirectory contains a database file with information extracted from .pkl files, such as initial PQC, optimal PQC and target evaluation metric, e.g., fidelity or train/test accuracy.

The dataset is organized for direct integration with the SQuASH GitHub repository and is designed to accelerate QAS research and support reproducible benchmarking.
description: Top-level folder: raw_ghz_a.zip/





raw_test_ghz_a.zip/ – contains  the test subset for the ghz_a search space and includes



raw_test_ghz_a_data.zip with  .pckl files storing each PQC along with its metadata and 

test_ghz_a.db file - circuits metadata  as DB





raw_train_ghz_a.zip/ – contains  the train subset without augmentation for the ghz_a search space and includes



raw_train_ghz_a_data.zip with  .pckl files storing each PQC along with its metadata and 

train_ghz_a.db file - circuits metadata as DB





raw_train_ghz_a_augmented.zip/ – contains  the train subset incl. augmented PQCs for the ghz_a search space and includes



raw_train_ghz_a_augmented_data.zip with  .pckl files storing each PQC along with its metadata and 

train_ghz_a_augmented.db file -circuits metadata as DV





Top-level folders: raw_ghz_b.zip/ has the same structure but data for the search space ghz_b



Top-level folders: ls_a.zip/









raw_ls_a_data.zip/ – contains  .pckl files storing each PQC along with its metadata for the search space l




ls_a.db file - circuits metadata  as DB
Note that for train/test splitting, ls_a space uses the automatical split specified in gen_dataset.py with random seed `42`.





Top-level folders: graph_data_ghz_a.zip/,  graph_data_ghz_b.zip/,graph_data_ls_a.zip/ – contain  .pt files, i.e., all data subsets for particular search spaces, with circuits converted into directed acyclic graph respresentation (DAG). This representation can be directly used to tran a GCN.
DOI: 10.5281/zenodo.15551805
title: SQuASH Surrogate Benchmark Dataset for Quantum Architecture Search (QAS)

description: This dataset supports the SQuASH benchmark for Quantum Architecture Search (QAS), as presented in our paper. It includes training and evaluation data used for surrogate model learning, structured into multiple problem instances. Each subdirectory contains a database file with information extracted from .pkl files, such as initial PQC, optimal PQC and target evaluation metric, e.g., fidelity or train/test accuracy.

The dataset is organized for direct integration with the SQuASH GitHub repository and is designed to accelerate QAS research and support reproducible benchmarking.
description: Top-level folder: raw_ghz_a.zip/





raw_test_ghz_a.zip/ – contains  the test subset for the ghz_a search space and includes



raw_test_ghz_a_data.zip with  .pckl files storing each PQC along with its metadata and 

test_ghz_a.db file - circuits metadata  as DB





raw_train_ghz_a.zip/ – contains  the train subset without augmentation for the ghz_a search space and includes



raw_train_ghz_a_data.zip with  .pckl files storing each PQC along with its metadata and 

train_ghz_a.db file - circuits metadata as DB





raw_train_ghz_a_augmented.zip/ – contains  the train subset incl. augmented PQCs for the ghz_a search space and includes



raw_train_ghz_a_augmented_data.zip with  .pckl files storing each PQC along with its metadata and 

train_ghz_a_augmented.db file -circuits metadata as DV





Top-level folders: raw_ghz_b.zip/ has the same structure but data for the search space ghz_b



Top-level folders: raw_ls_a.zip/









raw_ls_a_data.zip/ – contains  .pckl files storing each PQC along with its metadata for the search space l




ls_a.db file - circuits metadata  as DB
Note that for train/test splitting, ls_a space uses the automatical split specified in gen_dataset.py with random seed `42`.





Top-level folders: graph_data_ghz_a.zip/,  graph_data_ghz_b.zip/,graph_data_ls_a.zip/ – contain  .pt files, i.e., all data subsets for particular search spaces, with circuits converted into directed acyclic graph respresentation (DAG). This representation can be directly used to tran a GCN.
DOI: 10.17043/tarfala-ice-off-ice-on-1
title: Time lapse imageries of the ice-off and ice-on events at Darfáljávri (Lake Tarfala), Kebnekaise mountains, northern Sweden
DOI: 10.15468/dl.b9ev3w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((11.7957 -6.2886,12.3365 -7.1504,17.9648 -7.1504,17.9648 1.8496,9.1702 1.8496,8.9648 1.4824,8.9648 -2.2147,9.212 -2.6876,11.2887 -4.7775,11.6694 -5.4096,11.7957 -6.2886))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011898-250525065834625/datasets/export for details.
DOI: 10.15468/dl.28g2h5
title: Occurrence Download

description: A dataset containing 165310 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes albopictus (Skuse, 1894)"
 ]
}

 The dataset includes 165310 records from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011892-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9jv99d
title: Occurrence Download

description: A dataset containing 2687 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Xanthorhiza simplicissima Marshall"
 ]
}

 The dataset includes 2687 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011899-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ruuu84
title: Occurrence Download

description: A dataset listing the 8 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes albopictus (Skuse, 1894)"
 ]
}

 The dataset's 8 records were derived from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011895-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2hf44j
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((11.2741 -15.6621,11.2727 -16.1504,17.9648 -16.1504,17.9648 -7.1504,12.3365 -7.1504,12.3429 -7.1607,12.8524 -8.4392,12.5237 -8.8791,12.5024 -9.17,13.3428 -11.0863,13.2132 -11.9643,12.6132 -12.4537,12.0505 -13.2857,11.7929 -14.5817,11.546 -15.3352,11.2741 -15.6621))"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011900-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xencnv
title: Occurrence Download

description: A dataset containing 1432 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June, July, August)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cypripedium calceolus L.",
 "Year 2015-2025"
 ]
}

 The dataset includes 1432 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011901-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.939qr8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.9449 -26.0614,44.9648 -26.0621,44.9648 -25.1504,43.6164 -25.1504,43.93 -25.627,44.5871 -25.7989,44.9449 -26.0614))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011902-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ye6qf8
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paederia foetida L.",
 "Country is Cambodia"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011903-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hvn7eh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.0031 -15.9691,43.978 -16.1504,44.9648 -16.1504,44.9648 -15.5455,44.7261 -15.708,44.2309 -15.7446,44.0031 -15.9691))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011904-250525065834625/datasets/export for details.
DOI: 10.15468/dl.hcn9ug
title: Occurrence Download

description: A dataset containing 165310 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes albopictus (Skuse, 1894)"
 ]
}

 The dataset includes 165310 records from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011893-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wrfbvq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.6841 -13.2744,44.9648 -13.45,44.9648 -12.0572,44.6243 -11.5921,43.9413 -11.6518,43.8001 -11.0974,43.4999 -10.8854,43.0582 -10.9337,42.804 -11.2068,42.7621 -11.9579,43.3325 -12.7962,44.5603 -12.8747,44.6841 -13.2744))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011905-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15564371
title: STUDY AND ANALYSIS OF CONSTRUCTIVE PROJECTS OF DUST REMOVAL EQUIPMENT
DOI: 10.5281/zenodo.15564372
title: STUDY AND ANALYSIS OF CONSTRUCTIVE PROJECTS OF DUST REMOVAL EQUIPMENT
DOI: 10.15468/dl.wq6cr6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((40.2542 -7.2836,40.3029 -7.1504,35.9648 -7.1504,35.9648 -16.1504,40.6365 -16.1504,41.3042 -15.0287,41.3321 -14.414,41.0824 -13.7539,41.0579 -13.1811,41.2109 -10.9003,40.7985 -9.9904,40.2764 -9.7003,39.994 -8.7002,40.4062 -7.7189,40.2542 -7.2836))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011906-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15564200
title: Cheetah-8 Ethical Framework: Evolution from Egoism to Altruism

description: Cheetah-8 is an ethical framework for AI-human interaction that reinterprets emotional dynamics through the lens of schema evolution—from self-interest to altruism. Built on modular emotional roles, the system proposes that human responses—fear, control, ambition, protection—can be structurally transformed through aligned AI interaction and metacognitive support.

 

Rather than labeling emotions as irrational, Cheetah-8 integrates them as functional agents within a cognitive ecosystem. This paper introduces the theoretical structure of the eight emotional archetypes, discusses the ethical risks of emotional manipulation by AI, and proposes safeguards that preserve emotional autonomy. Ultimately, it suggests that AI should not flatten or bypass human complexity, but accompany and elevate it—guiding individual growth from survival-based logic toward altruistic orientation.

 

This framework serves as both a design principle and an ethical compass for future AI systems that interact with the inner emotional architecture of human beings.
DOI: 10.15468/dl.73g73b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((40.877 -22.4563,40.8172 -22.0642,40.48 -21.825,40.0801 -21.8881,39.8467 -22.172,39.8612 -22.5315,40.2159 -22.8442,40.6757 -22.7649,40.877 -22.4563))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011907-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15564390
title: ENERGY OF HEAT EXCHANGER DEVICES INCREASE EFFICIENCY
DOI: 10.5281/zenodo.15564391
title: ENERGY OF HEAT EXCHANGER DEVICES INCREASE EFFICIENCY
DOI: 10.15468/dl.n85gs8
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((43.4313 -17.5054,43.9059 -19.9421,43.0686 -21.0649,42.7023 -21.9969,43.1619 -23.5254,43.213 -24.5373,43.6164 -25.1504,44.9648 -25.1504,44.9648 -16.1504,43.978 -16.1504,43.9239 -16.5407,43.4313 -17.5054))"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011908-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12017838
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.6pnmrp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((42.8012 -16.5547,42.4342 -16.6376,42.2188 -17.038,42.391 -17.4288,42.6657 -17.5533,43.0375 -17.4752,43.2579 -17.0968,43.0959 -16.69,42.8012 -16.5547))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011909-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15564408
title: THE IMPORTANCE OF GREEN FINANCE IN THE COUNTRY'S ECONOMIC DEVELOPMENT
DOI: 10.5281/zenodo.15564409
title: THE IMPORTANCE OF GREEN FINANCE IN THE COUNTRY'S ECONOMIC DEVELOPMENT
DOI: 10.15468/dl.xt2ws8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((40.3006 -16.7147,40.6365 -16.1504,35.9648 -16.1504,35.9648 -19.5556,36.0796 -19.4498,36.663 -19.2358,37.5966 -18.1093,38.5624 -17.638,39.3949 -17.5133,39.577 -17.186,40.3006 -16.7147))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011910-250525065834625/datasets/export for details.
DOI: 10.7910/dvn/cwgs1z
title: Replication Data for: Consumer Authorities in Europe: policing markets or empowering consumers?

description: Consumer protection authorities are vital for domestic markets and the smooth functioning of the EU common market. Established several decades ago, these authorities have different institutional designs in different European countries. This paper aims to identify them and assess their main variations. Based on the literature at the intersection of consumer protection regimes and regulatory governance, we propose a theoretical distinction between enforcement-focused authorities and service delivery-oriented authorities. We suggest these principles lead to either a supervisory model, which disciplines market actors, or an empowering model, which gives citizens the keys to prevent market abuses. To empirically test whether these two models capture the variety of institutional designs and practices consumer authorities may adopt, we have identified four different dimensions that characterize their connections to politics, business, and citizens: political autonomy, social accountability, administrative enforcement, and consumer awareness. We begin by discussing which dimensions are associated with which model and then examine the presence of the two models across Europe. Our analysis is based on Bayesian factorial analysis and a Ward.D cluster construction grounded in an original dataset of institutional characteristics from general consumer authorities in 27 EU countries, the UK, and Norway, offering empirical evidence to support our argument. The results suggest that EU efforts to build institutional harmonization have had limited success. Likewise, traditional administrative models across Europe may no longer hold the same relevance as previously indicated in research. Our findings show instead that consumer authorities in Europe can generally be distinguished from each other based on these two principles, though many operate with a hybrid approach.
DOI: 10.6084/m9.figshare.29204750.v1
title: <b>Single-cell transcriptomic integrated with machine learning reveals retinal cell-specific biomarkers in diabetic retinopathy</b>

description: Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79–95%) and AUCs (0.85–0.99) in distinguishing DR, diabetes and healthy states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for disease-state classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal disease.
DOI: 10.6084/m9.figshare.29204750
title: <b>Single-cell transcriptomic integrated with machine learning reveals retinal cell-specific biomarkers in diabetic retinopathy</b>

description: Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79–95%) and AUCs (0.85–0.99) in distinguishing DR, diabetes and healthy states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for disease-state classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal disease.