cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18338077
title: Beyond the Training Domain: Robust Generative Transition State Models for Unseen Chemistry

description: Pretrained models as well as the curated datasets.



AEFM: configs and database to train and run inference using adaptive equilibrium flow matching.

OAReactDiff: codebase and training script to finetune the published OAReactDiff checkpoint with the new embedding layer, as well as with the newly developed self-supervised scheme.

ReactOT: codebase and training/inference script to train and sample with ReactOT

checkpoint_: Checkpoints for all models used in the work
DOI: 10.15468/dl.spjpqn
title: Occurrence Download

description: A dataset containing 1613 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Technomyrmex albipes, Leptogenys diminuta, Vollenhovia emeryi, Paratrechina vividula, Tetramorium lanuginosum, Pheidole gatesi, Pheidole plagiaria, Monomorium chinense, Pheidole planifrons, Lasius hayashi, Lasius japonicus, Pristomyrmex punctatus, Crematogaster rogenhoferi, Tetramorium wroughtonii, Polyrhachis moesta, Polyrhachis rastellata, Polyrhachis subpilosa, Polyrhachis vigilans, Tetraponera attenuata, Anochetus risii, Aphaenogaster exasperata, Aphaenogaster feae, Aphaenogaster hunanensis, Aphaenogaster rothneyi, Aphaenogaster schurri, Camponotus bedoti, Camponotus compressus, Camponotus helvus, Camponotus irritans, Camponotus itoi, Camponotus jianghuaensis, Camponotus keihitoi, Camponotus nipponensis, Camponotus parius, Camponotus selene, Camponotus singularis, Camponotus variegatus, Camponotus wasmanni, Cataulacus granulatus, Dilobocondyla fouqueti, Dolichoderus feae, Dolichoderus flatidorsus, Dolichoderus incisus, Dolichoderus sibiricus, Dolichoderus taprobanae, Vollenhovia acanthina, Harpegnathos venator, Hypoponera confinis, Hypoponera sauteri, Leptogenys kitteli, Leptogenys peuqueti, Liometopum sinense, Lasius coloratus, Messor aciculatus, Monomorium floricola, Monomorium intrudens, Monomorium orientale)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Country is one of (China, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1613 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011774-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tb2rrh
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Codiocarpus merrittii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 24 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011775-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xte7vg
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Unaspis yanonensis"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011777-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14241668
title: Seismic refraction, reflection and free-air gravity data of OBS2020-3 in the southwest sub-basin, South China Sea

description: This dataset (OBS2020-3.Files.zip) contains SEGY files of the OBS2020-3 and the NW section of the MCS2020-3 profiles, as well as the free-air gravity anomaly data along the seismic profiles. The time-axis of the SEGY files for the ocean bottom seismometers are reduced by a reduction velocity of 6.0 km/s.
DOI: 10.5281/zenodo.14241669
title: Seismic refraction, reflection and free-air gravity data of OBS2020-3 in the southwest sub-basin, South China Sea

description: This dataset (OBS2020-3.Files.zip) contains SEGY files of the OBS2020-3 and the NW section of the MCS2020-3 profiles, as well as the free-air gravity anomaly data along the seismic profiles. The time-axis of the SEGY files for the ocean bottom seismometers are reduced by a reduction velocity of 6.0 km/s.
DOI: 10.18150/xzngvp
title: Effects of early inertial exercises on morphological and functional recovery after ACL reconstruction.

description: Celem niniejszego badania było porównanie skuteczności dwóch protokołów rehabilitacji u pacjentów po rekonstrukcji więzadła krzyżowego przedniego. Grupa pierwsza (SR) przeszła standardową rehabilitację, natomiast grupa eksperymentalna (SRI) wykonywała standardową rehabilitację oraz dodatkowe ćwiczenia inercyjne, począwszy od siódmego tygodnia rehabilitacji. Ćwiczenia inercyjne wdrożono w konfiguracji zamkniętego łańcucha kinetycznego, aby zapewnić bezpieczeństwo we wczesnej fazie pooperacyjnej. Po 12 tygodniach rehabilitacji wyniki oceniono za pomocą testów siły izokinetycznej i inercyjnej, dynamicznej oceny równowagi, pomiarów obwodu uda oraz analizy składu ciała.
DOI: 10.15468/dl.mmy229
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-122.28427 30.20081,-65.71573 30.20081,-65.71573 52.79919,-122.28427 52.79919,-122.28427 30.20081))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mertensia alpina"
 ]
}

 The dataset includes 174 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011778-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/xeeucz
title: HBRG Insects Dataset Vol 2

description: All records of insects submitted to HBRG after 6 March 2022, plus any records originally in HBRG Insects Dataset Vol 1 that required editing. Assembly and mobilisation of this dataset has been funded by grants from Scottish Natural Heritage (now NatureScot) since 2004, and in some years by members of the Highland Biodiversity Partnership.
DOI: 10.5281/zenodo.18251610
title: Daily snow depth on Arctic sea ice during the winter season from AMSR-E&2 data (2002 – 2024)

description: This dataset contains a daily snow depth on sea ice for the winter months (October - April) in the Arctic Ocean. All variables are gridded on a NSIDC Sea Ice Polar Stereographic North (EPSG:3413) at a spatial resolution of 25 km. The snow depth is derived from the newly developed algorithm proposed by He et al. (2024), which considers the influences of snow density on the relationship between gradient ratio of 6.9 and 18.7 GHz vertically polarized TBs [i.e., GRV(19/7)].
DOI: 10.5281/zenodo.18251609
title: Daily snow depth on Arctic sea ice during the winter season from AMSR-E&2 data (2002 – 2024)

description: This dataset contains a daily snow depth on sea ice for the winter months (October - April) in the Arctic Ocean. All variables are gridded on a NSIDC Sea Ice Polar Stereographic North (EPSG:3413) at a spatial resolution of 25 km. The snow depth is derived from the newly developed algorithm proposed by He et al. (2024), which considers the influences of snow density on the relationship between gradient ratio of 6.9 and 18.7 GHz vertically polarized TBs [i.e., GRV(19/7)].
DOI: 10.15468/dl.7336dk
title: Occurrence Download

description: A dataset containing 417 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Astragalus adsurgens"
 ]
}

 The dataset includes 417 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011779-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2jq3wh
title: Occurrence Download

description: A dataset containing 517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anemone patens"
 ]
}

 The dataset includes 517 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011780-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18372768
title: Kurnia m

description: Analisis kebutuhan sarana dan prasarana di kabupaten luwu
DOI: 10.5281/zenodo.18372769
title: Kurnia m

description: Analisis kebutuhan sarana dan prasarana di kabupaten luwu
DOI: 10.6084/m9.figshare.27905159
title: Reactivity Impact of Updated <sup>35</sup>Cl Nuclear Data on the Molten Chloride Reactor Experiment

description: The Molten Chloride Reactor Experiment (MCRE) will be built and operated as part of an Advanced Reactor Demonstration Project Risk Reduction Award. This fast spectrum, chloride salt–fueled molten salt reactor is sensitive to chlorine nuclear data. Recent experimental measurements of the <sup>35</sup>Cl (n,p) cross section disagree with <sup>35</sup>Cl nuclear data evaluations outside the bounds of the nuclear data uncertainty covariance matrices. To address this challenge, Los Alamos National Laboratory has been charged with measuring the <sup>35</sup>Cl (n,p) cross section at the Los Alamos Neutron Science Center and generating an updated <sup>35</sup>Cl nuclear data evaluation integrating recent cross-section measurements. This paper evaluates the reactivity impact of using the updated <sup>35</sup>Cl evaluation on the MCRE application compared to publicly available evaluations (ENDF/B up to VIII.0 and JENDL-5).
DOI: 10.6084/m9.figshare.27905159.v1
title: Reactivity Impact of Updated <sup>35</sup>Cl Nuclear Data on the Molten Chloride Reactor Experiment

description: The Molten Chloride Reactor Experiment (MCRE) will be built and operated as part of an Advanced Reactor Demonstration Project Risk Reduction Award. This fast spectrum, chloride salt–fueled molten salt reactor is sensitive to chlorine nuclear data. Recent experimental measurements of the <sup>35</sup>Cl (n,p) cross section disagree with <sup>35</sup>Cl nuclear data evaluations outside the bounds of the nuclear data uncertainty covariance matrices. To address this challenge, Los Alamos National Laboratory has been charged with measuring the <sup>35</sup>Cl (n,p) cross section at the Los Alamos Neutron Science Center and generating an updated <sup>35</sup>Cl nuclear data evaluation integrating recent cross-section measurements. This paper evaluates the reactivity impact of using the updated <sup>35</sup>Cl evaluation on the MCRE application compared to publicly available evaluations (ENDF/B up to VIII.0 and JENDL-5).
DOI: 10.15468/dl.4b9bd8
title: Occurrence Download

description: A dataset containing 3147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Bursera simaruba",
 "Year 2012-2026"
 ]
}

 The dataset includes 3147 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011781-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.te6jfe
title: Occurrence Download

description: A dataset containing 435 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cecropia obtusifolia",
 "Year 2013-2026"
 ]
}

 The dataset includes 435 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011782-260120142942310/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/14621172
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.kb36k6
title: Occurrence Download

description: A dataset containing 315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pinus chiapensis"
 ]
}

 The dataset includes 315 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011783-260120142942310/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.27854612
title: Functional Projection <i>K</i>-means

description: A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a functional <i>K</i>-means. The subspace and the partition are estimated simultaneously by minimizing the within deviance in the reduced space. This allows us to find new dimensions with a very low within deviance, which should correspond to a high level of discriminant power. However, in some cases, the total deviance explained by the new dimensions is so low as to make the subspace, and therefore the partition identified in it, insignificant. To overcome this drawback, we add to the loss a penalty equal to the negative total deviance in the reduced space. In this way, subspaces with a low deviance are avoided. We show how several existing methods are particular cases of our proposal simply by varying the weight of the penalty. The estimation is improved by adding a regularization term to the loss in order to take into account the functional nature of the data by smoothing the centroids. In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. The effectiveness of our proposal is demonstrated through its application to both real and simulated data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27854612.v2
title: Functional Projection <i>K</i>-means

description: A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a functional <i>K</i>-means. The subspace and the partition are estimated simultaneously by minimizing the within deviance in the reduced space. This allows us to find new dimensions with a very low within deviance, which should correspond to a high level of discriminant power. However, in some cases, the total deviance explained by the new dimensions is so low as to make the subspace, and therefore the partition identified in it, insignificant. To overcome this drawback, we add to the loss a penalty equal to the negative total deviance in the reduced space. In this way, subspaces with a low deviance are avoided. We show how several existing methods are particular cases of our proposal simply by varying the weight of the penalty. The estimation is improved by adding a regularization term to the loss in order to take into account the functional nature of the data by smoothing the centroids. In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. The effectiveness of our proposal is demonstrated through its application to both real and simulated data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27893376
title: Local Clustering for Functional Data

description: In functional data analysis, unsupervised clustering has been extensively conducted and has important implications. In most of the existing functional clustering analyses, it is assumed that there is a single clustering structure across the whole domain of measurement (say, time interval). In some data analyses, for example, the analysis of normalized COVID-19 daily confirmed cases for the U.S. states, it is observed that functions can have different clustering patterns in different time subintervals. To tackle the lack of flexibility of the existing functional clustering techniques, we develop a local clustering approach, which can fully data-dependently identify subintervals, where, in different subintervals, functions have different clustering structures. This approach is built on the basis expansion technique and has a novel penalization form. It simultaneously achieves subinterval identification, clustering, and estimation. Its estimation and clustering consistency properties are rigorously established. In simulation, it significantly outperforms multiple competitors. In the analysis of the COVID-19 case trajectory data, it identifies sensible subintervals and clustering structures. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27893376.v1
title: Local Clustering for Functional Data

description: In functional data analysis, unsupervised clustering has been extensively conducted and has important implications. In most of the existing functional clustering analyses, it is assumed that there is a single clustering structure across the whole domain of measurement (say, time interval). In some data analyses, for example, the analysis of normalized COVID-19 daily confirmed cases for the U.S. states, it is observed that functions can have different clustering patterns in different time subintervals. To tackle the lack of flexibility of the existing functional clustering techniques, we develop a local clustering approach, which can fully data-dependently identify subintervals, where, in different subintervals, functions have different clustering structures. This approach is built on the basis expansion technique and has a novel penalization form. It simultaneously achieves subinterval identification, clustering, and estimation. Its estimation and clustering consistency properties are rigorously established. In simulation, it significantly outperforms multiple competitors. In the analysis of the COVID-19 case trajectory data, it identifies sensible subintervals and clustering structures. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27854612.v1
title: Functional projection <i>K</i>-means

description: A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a functional <i>K</i>-means. The subspace and the partition are estimated simultaneously by minimizing the within deviance in the reduced space. This allows us to find new dimensions with a very low within deviance, which should correspond to a high level of discriminant power. However, in some cases, the total deviance explained by the new dimensions is so low as to make the subspace, and therefore the partition identified in it, insignificant. To overcome this drawback, we add to the loss a penalty equal to the negative total deviance in the reduced space. In this way, subspaces with a low deviance are avoided. We show how several existing methods are particular cases of our proposal simply by varying the weight of the penalty. The estimation is improved by adding a regularization term to the loss in order to take into account the functional nature of the data by smoothing the centroids. In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. The effectiveness of our proposal is demonstrated through its application to both real and simulated data. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.18372009
title: Supplementary Materials for: "Comparative Analysis of YOLOv8 and YOLOv11 Models for Phenotypic Traits of Edible Mushrooms"

description: This record contains supplementary materials associated with the manuscript"Comparative Analysis of YOLOv8 and YOLOv11 Models for Phenotypic Traits of Edible Mushrooms".

The archive includes Table S1–S2 which presenting qualitative comparison of predicted and measured phenotypic trait in Pleurotus ostreatus and Flammulina velutipes, and Figures S1–S4 which presenting qualitative segmentation results of YOLOv8 and YOLOv11 models for Pleurotus ostreatus and Flammulina velutipes, additional prediction examples, and analysis scripts.
DOI: 10.5281/zenodo.18372008
title: Supplementary Materials for: "Comparative Analysis of YOLOv8 and YOLOv11 Models for Phenotypic Traits of Edible Mushrooms"

description: This record contains supplementary materials associated with the manuscript"Comparative Analysis of YOLOv8 and YOLOv11 Models for Phenotypic Traits of Edible Mushrooms".

The archive includes Table S1–S2 which presenting qualitative comparison of predicted and measured phenotypic trait in Pleurotus ostreatus and Flammulina velutipes, and Figures S1–S4 which presenting qualitative segmentation results of YOLOv8 and YOLOv11 models for Pleurotus ostreatus and Flammulina velutipes, additional prediction examples, and analysis scripts.
DOI: 10.15468/dl.mn4hmr
title: Occurrence Download

description: A dataset containing 1467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Discophora guianensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1467 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011784-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18372824
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2026-01-26 — cumulative data.