cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17713834
title: James Baldwin's 1965 Debate Speech at Cambridge Union - Prosodic–Musical-Rhetorical Analysis Dataset (v1.0)

description: This dataset contains prosodic, phonetic, musical, and rhetorical analysis of James Baldwin’s 1965 Cambridge Union debate speech on the motion “The American Dream is at the Expense of the American Negro.” Generated using the Rhetoric Analysis Toolkit, the dataset integrates forced phonetic alignment (Montreal Forced Aligner), acoustic feature extraction (Praat via Parselmouth), AI-generated rhetorical scheme detection (OpenAI GPT-4o), and musicological representations (MIDI and MusicXML). This multimodal approach enables researchers to investigate how Baldwin’s vocal performance—pitch, timing, intensity, pacing, phrasing, and prosodic emphasis—interacts with classical rhetorical structures to construct one of the most influential speeches of the civil-rights era.

The dataset includes aligned audio and TextGrid files, detailed acoustic analyses at both word and phoneme levels, rhetorical-scheme annotations across all 38 segments of the speech (including anaphora, assonance, alliteration, homoioteleuton, isocolon, rhyme, and polysyndeton/asyndeton), and cross-segment patterns such as ploce and extended anaphora. Advanced pattern-specific analyses examine the recurring rhetorical force of key lexical anchors such as “dream,” “shock,” and “you,” providing formant trajectories (F1–F4), pitch contours, and spectral information. Musicological analyses translate prosodic movement into notated contours, MIDI sequences, and MusicXML files suitable for computational or artistic use.

This dataset is designed for scholars in digital humanities, rhetoric, African American studies, political communication, linguistics, musicology, and performance studies. It enables close investigation of how Baldwin’s acoustic delivery structures argument, intensifies affect, and produces the moral and political force for which this speech is known.
DOI: 10.5281/zenodo.17882760
title: James Baldwin's 1965 Debate Speech at Cambridge Union - Prosodic–Musical-Rhetorical Analysis Dataset (v1.0)

description: This dataset contains prosodic, phonetic, musical, and rhetorical analysis of James Baldwin’s 1965 Cambridge Union debate speech on the motion “The American Dream is at the Expense of the American Negro.” Generated using the Rhetoric Analysis Toolkit, the dataset integrates forced phonetic alignment (Montreal Forced Aligner), acoustic feature extraction (Praat via Parselmouth), AI-generated rhetorical scheme detection (OpenAI GPT-4o), and musicological representations (MIDI and MusicXML). This multimodal approach enables researchers to investigate how Baldwin’s vocal performance—pitch, timing, intensity, pacing, phrasing, and prosodic emphasis—interacts with classical rhetorical structures to construct one of the most influential speeches of the civil-rights era.

The dataset includes aligned audio and TextGrid files, detailed acoustic analyses at both word and phoneme levels, rhetorical-scheme annotations across all 38 segments of the speech (including anaphora, assonance, alliteration, homoioteleuton, isocolon, rhyme, and polysyndeton/asyndeton), and cross-segment patterns such as ploce and extended anaphora. Advanced pattern-specific analyses examine the recurring rhetorical force of key lexical anchors such as “dream,” “shock,” and “you,” providing formant trajectories (F1–F4), pitch contours, and spectral information. Musicological analyses translate prosodic movement into notated contours, MIDI sequences, and MusicXML files suitable for computational or artistic use.

This dataset is designed for scholars in digital humanities, rhetoric, African American studies, political communication, linguistics, musicology, and performance studies. It enables close investigation of how Baldwin’s acoustic delivery structures argument, intensifies affect, and produces the moral and political force for which this speech is known.
DOI: 10.15468/dl.tde2by
title: Occurrence Download

description: A dataset containing 352 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Armandia intermedia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 352 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045217-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qk93tm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((122.07 -3.312833, 121.99962 -3.318397, 121.93097 -3.334953, 121.865739 -3.362093, 121.805531 -3.39915, 121.751828 -3.445213, 121.705951 -3.499149, 121.66903 -3.55963, 121.641975 -3.625169, 121.625455 -3.694153, 121.619877 -3.764883, 121.625383 -3.83562, 121.641839 -3.90462, 121.668842 -3.970184, 121.70573 -4.030697, 121.751596 -4.084668, 121.805311 -4.130766, 121.865551 -4.167855, 121.930834 -4.195021, 121.999548 -4.211593, 122.07 -4.217163, 122.140452 -4.211593, 122.209166 -4.195021, 122.274449 -4.167855, 122.334689 -4.130766, 122.388404 -4.084668, 122.43427 -4.030697, 122.471158 -3.970184, 122.498161 -3.90462, 122.514617 -3.83562, 122.520123 -3.764883, 122.514545 -3.694153, 122.498025 -3.625169, 122.47097 -3.55963, 122.434049 -3.499149, 122.388172 -3.445213, 122.334469 -3.39915, 122.274261 -3.362093, 122.20903 -3.334953, 122.14038 -3.318397, 122.07 -3.312833))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045216-251120083545085/datasets/export for details.
DOI: 10.7910/dvn/h5s0og
title: Replication Data for: Ascertaining acute kidney injury in children hospitalized with acute malnutrition: operationalizing definitions of kidney injury and identifying children at risk

description: Data on a prospective observational study of children hospitalized with acute malnutrition at Mulago National Referral Hospital with repeat creatinine values and outcome ascertainment to define acute kidney injury.
DOI: 10.15151/esrf-es-2301223947
title: UGA Master 2 Praticals : uLaue

description: This is a practical for Master 2 student from the Matière Quantique master from the UGA, dedicated to the uLaue technique on typical standard test samples
DOI: 10.25907/00954
title: Data from: Transcription Factor Analysis of Rhodophytes Suggests Trihelix Transcription Factors Across the Florideophyceae

description: Transcription factors (TFs) are important gene transcription regulators involved in myriad functions such as development, metabolism, and stress response, and are found in all eukaryotes, with many families of TFs unique to plants and algae. Algae are of interest due to a wide range of novel metabolites of which TFs play an important role in regulating their biosynthesis. In particular, the red algae (phylum Rhodophyta) are a source of several important metabolites that are a current focus of further research. However, to date, investigations of TF families in rhodophytes have been limited due to the relative lack of genomic resources available and the small number of in-silico analyse of their TFs. In this study, we used genomic and transcriptomic data to identify rhodophyte TFs. We found that the general proportion of TFs in rhodophytes was overall consistent with previous research. However, for the first time in the rhodophyte class Florideophyceae, we report the presence of a putative TF within the trihelix TF (TTF) family, which are light-sensitive TFs associated with growth and stress response. In particular, we demonstrate evidence suggesting the presence of TTFs in three Asparagopsis taxiformis genomes. This was supported by analyses including Neighbour-Joining phylogeny, protein structure prediction, and motif analysis. In summary, this research reported the repertoire of TFs in rhodophyte algae across a much greater range than previously reported and identified putative TTFs in several algae from the class Florideophyceae. This opens an avenue for further research into the evolution of various TFs in early plants, as well as key regulatory factors in rhodophyte metabolism, though future research will be required to confirm these findings.
DOI: 10.15468/dl.pdef9f
title: Occurrence Download

description: A dataset containing 787975 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is one of (Liste rouge des Odonates de France-Liste rouge des Odonates de métropole 2016, Collecte et gestion des données de sciences participatives sur les Odonates en Limousin-Ensemble des données, Données issues du portail Sigogne - Bourgogne-Franche-Comté - TAXA Invertébrés, Atlas des papillons de jour et des libellules du Languedoc-Roussillon-Données CEN L-R papillons/libellule)",
 "OccurrenceStatus is Present",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862",
 "TaxonKey is Odonata"
 ]
}

 The dataset includes 787975 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045206-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ff344
title: Occurrence Download

description: A dataset containing 6550115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-85.13779 9.92726,-84.05747 9.92726,-84.05747 11.01813,-85.13779 11.01813,-85.13779 9.92726))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2020-2024"
 ]
}

 The dataset includes 6550115 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045184-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30850550
title: Factor Augmented Matrix Regression

description: We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, utilizing techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated solution for penalized matrix factor regression. Both algorithms are supported by established statistical and numerical convergence properties. Empirical evaluations conducted on synthetic and real economics datasets demonstrate FAMAR’s superiority in terms of accuracy, interpretability, and computational speed. An application to economic data showcases how matrix factors can be incorporated to predict the GDPs of the countries of interest and the influence of these factors on the GDPs.
DOI: 10.6084/m9.figshare.30850550.v1
title: Factor Augmented Matrix Regression

description: We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, utilizing techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated solution for penalized matrix factor regression. Both algorithms are supported by established statistical and numerical convergence properties. Empirical evaluations conducted on synthetic and real economics datasets demonstrate FAMAR’s superiority in terms of accuracy, interpretability, and computational speed. An application to economic data showcases how matrix factors can be incorporated to predict the GDPs of the countries of interest and the influence of these factors on the GDPs.
DOI: 10.6084/m9.figshare.30675098
title: Discovery of isatin-thiazole conjugates as potent urease inhibitors; synthesis, biochemical screening and computational studies

description: Urease is essential to <i>Helicobacter pylori</i> metabolism and plays role in stomach cancer, gastritis, peptic ulcer, hepatic coma, urinary tract infection, liver encephalopathy, and pyelonephritis. Therefore, inhibition of urease is an appealing approach to treat bacterial infections. The present work describes the synthesis of a series of ten new bioactive isatin-thiazole conjugates (<b>5a-j</b>). The target adducts were characterized using fourier transform infrared (FT-IR), <sup>1</sup>H- and <sup>13</sup>C- nuclear magnetic resonance imaging (NMR) spectroscopy. The compounds were obtained using a multistep strategy that included nitration, alkylation, condensation and cyclization sequence. Subsequently, these compounds were screened for their urease inhibition potential. All the compounds showed better inhibitory potential than the positive control, thiourea with IC<sub>50</sub> ranging from 0.44 to 8.70 µM. However, compound <b>5j</b> exhibited an excellent non-competitive urease inhibitory effect with an IC<sub>50</sub> value of 0.44 ± 0.23 µM. Apart from <i>in vitro</i> investigation, the molecular docking revealed a strong affinity of <b>5j</b> within the active site of urease exhibiting a binding energy of -7.9 kcal/mol. Succinctly, the lead inhibitor <b>5j</b> exhibited noteworthy IC<sub>50</sub> and effective binding free energy which emphasizes its strong binding potential.
DOI: 10.6084/m9.figshare.30675098.v1
title: Discovery of isatin-thiazole conjugates as potent urease inhibitors; synthesis, biochemical screening and computational studies

description: Urease is essential to <i>Helicobacter pylori</i> metabolism and plays role in stomach cancer, gastritis, peptic ulcer, hepatic coma, urinary tract infection, liver encephalopathy, and pyelonephritis. Therefore, inhibition of urease is an appealing approach to treat bacterial infections. The present work describes the synthesis of a series of ten new bioactive isatin-thiazole conjugates (<b>5a-j</b>). The target adducts were characterized using fourier transform infrared (FT-IR), <sup>1</sup>H- and <sup>13</sup>C- nuclear magnetic resonance imaging (NMR) spectroscopy. The compounds were obtained using a multistep strategy that included nitration, alkylation, condensation and cyclization sequence. Subsequently, these compounds were screened for their urease inhibition potential. All the compounds showed better inhibitory potential than the positive control, thiourea with IC<sub>50</sub> ranging from 0.44 to 8.70 µM. However, compound <b>5j</b> exhibited an excellent non-competitive urease inhibitory effect with an IC<sub>50</sub> value of 0.44 ± 0.23 µM. Apart from <i>in vitro</i> investigation, the molecular docking revealed a strong affinity of <b>5j</b> within the active site of urease exhibiting a binding energy of -7.9 kcal/mol. Succinctly, the lead inhibitor <b>5j</b> exhibited noteworthy IC<sub>50</sub> and effective binding free energy which emphasizes its strong binding potential.
DOI: 10.6084/m9.figshare.30850552
title: Low-Rank Online Dynamic Assortment with Dual Contextual Information

description: As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts – user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of O˜((d1+d2)rT), where d1,d2 represent the dimensions of the user and item features respectively, <i>r</i> is the rank of the parameter matrix, and <i>T</i> denotes the time horizon. This bound represents a substantial improvement over prior literature, achieved by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method.
DOI: 10.6084/m9.figshare.30850552.v1
title: Low-Rank Online Dynamic Assortment with Dual Contextual Information

description: As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts – user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of O˜((d1+d2)rT), where d1,d2 represent the dimensions of the user and item features respectively, <i>r</i> is the rank of the parameter matrix, and <i>T</i> denotes the time horizon. This bound represents a substantial improvement over prior literature, achieved by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method.
DOI: 10.6084/m9.figshare.30597199
title: Aurone-based α-glucosidase inhibitors for antidiabetic therapy: <i>in vitro</i>, <i>in vivo</i>, and <i>in silico</i> studies

description: This study aimed to evaluate the α-glucosidase inhibitory potential of newly synthesized aurone derivatives (1–14) using an integrated experimental and computational strategy, with emphasis on their antidiabetic potential. The compounds were evaluated through in vitro α-glucosidase inhibition and enzyme kinetic assays, along with <i>in</i> <i>vivo</i> studies to assess postprandial glucose control. Molecular docking, MM-GBSA calculations, and molecular dynamics (MD) simulations were performed to analyze interactions with diabetic targets (PDB IDs: 5NN4 and 6KK1). Furthermore, <i>in silico</i> ADME profiling and density functional theory (DFT) analyses were conducted to predict pharmacokinetic properties, drug-likeness, and electronic behavior. Several aurone derivatives exhibited strong α-glucosidase inhibition, surpassing standard drugs. Kinetic studies revealed a competitive inhibition mechanism, and <i>in</i> <i>vivo</i> evaluations confirmed their glucose-lowering effects – the first such report for aurones. Computational analyses indicated stable enzyme – ligand complexes with favorable binding affinities and ADME features. DFT results supported the observed structure – activity relationships and highlighted key electronic attributes influencing activity. This comprehensive study identifies aurones as potent α-glucosidase inhibitors with significant therapeutic potential, providing a strong foundation for further development of aurone-based antidiabetic agents.
DOI: 10.6084/m9.figshare.30597199.v1
title: Aurone-based α-glucosidase inhibitors for antidiabetic therapy: <i>in vitro</i>, <i>in vivo</i>, and <i>in silico</i> studies

description: This study aimed to evaluate the α-glucosidase inhibitory potential of newly synthesized aurone derivatives (1–14) using an integrated experimental and computational strategy, with emphasis on their antidiabetic potential. The compounds were evaluated through in vitro α-glucosidase inhibition and enzyme kinetic assays, along with <i>in</i> <i>vivo</i> studies to assess postprandial glucose control. Molecular docking, MM-GBSA calculations, and molecular dynamics (MD) simulations were performed to analyze interactions with diabetic targets (PDB IDs: 5NN4 and 6KK1). Furthermore, <i>in silico</i> ADME profiling and density functional theory (DFT) analyses were conducted to predict pharmacokinetic properties, drug-likeness, and electronic behavior. Several aurone derivatives exhibited strong α-glucosidase inhibition, surpassing standard drugs. Kinetic studies revealed a competitive inhibition mechanism, and <i>in</i> <i>vivo</i> evaluations confirmed their glucose-lowering effects – the first such report for aurones. Computational analyses indicated stable enzyme – ligand complexes with favorable binding affinities and ADME features. DFT results supported the observed structure – activity relationships and highlighted key electronic attributes influencing activity. This comprehensive study identifies aurones as potent α-glucosidase inhibitors with significant therapeutic potential, providing a strong foundation for further development of aurone-based antidiabetic agents.
DOI: 10.6084/m9.figshare.30621364
title: Synthesis, anticancer activity and computational studies of new benzimidazole-triazole-pyridine glycoside conjugates

description: The discovery of novel compounds as potential cancer drug candidates has garnered significant interest and widespread attention. A novel series of benzimidazole-1,2,3-triazole-pyridine-glycosyl hybrids was rationally designed and synthesized to explore their potential anticancer activity. The targeted compounds were achieved via click chemistry. The acetylated N<sup>1</sup>-glycosyl-1,2,3-triazoles were deprotected, producing the free hydroxy glycosides. Their cytotoxicity was evaluated against human colorectal (HCT-116), hepatic (HepG-2), and breast (MCF-7) cancers. Molecular docking and dynamics, in addition to Density functional theory (DFT) calculations, were studied. Glycosyl-1,2,3-triazoles <b>15</b> and <b>17</b> exhibited the highest cytotoxic activity among the tested compounds, while others demonstrated selective efficacy against specific cancer cell lines. Notably, compound <b>17</b> showed a 1.808-fold increase in cytotoxicity compared to doxorubicin when tested on MCF-7 breast cancer cells (IC<sub>50</sub> = 33.32 µM). Molecular docking studies with the epidermal growth factor receptor (EGFR) indicated favorable-binding interactions and potential inhibitory effects. Molecular dynamics further confirmed the stable integration of <b>17</b> within the EGFR active site, preserving the structural integrity of its catalytic domain. DFT calculations provided insights into the electronic structure, molecular orbitals, and electrostatic potential of compound <b>17</b>.