cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vdukx7
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stawellia gymnocephala",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 31 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008112-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eunqfk
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Utiaritichthys esguiceroi",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 55 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008114-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qs753s
title: Occurrence Download

description: A dataset containing 417208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Agrimonia eupatoria"
 ]
}

 The dataset includes 417208 records from 2623 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008008-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15301865
title: Improved Sea Level Reconstruction from 1900 to 2019

description: This dataset provides global mean sea level and regional sea level reconstructions derived from tide gauge records and satellite altimetry observations. It represents an improved version of the reconstruction by Church and White (2011), utilizing a reduced-space optimal interpolation technique. Enhancements include the incorporation of sea level fingerprints, sterodynamic sea level change patterns, and more comprehensive estimates of local vertical land motion.

### Data supplement for "Improved Sea Level Reconstruction from 1900 to 2019" by Jinping Wang, John A. Church, Xuebin Zhang, and Xianyao Chen.

Please email wangjinping@ouc.edu for questions.

### PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA ###Please cite “Improved Sea Level Reconstruction from 1900 to 2019” when using this data set.Our results rely on previous work, and please acknowledge the previous work by citing the original sources of the data. 

This data supplement contains the following files:GMSL_yr.nc & GMSL_yr.txtThe reconstructed global mean sea level time series from 1900 to 2019, along with the associated one standard deviation uncertainty.

Regional_yr.nc The regional sea level reconstruction fields from 1900 to 2019 at 1° spatial resolution.
DOI: 10.5281/zenodo.15301866
title: Improved Sea Level Reconstruction from 1900 to 2019

description: This dataset provides global mean sea level and regional sea level reconstructions derived from tide gauge records and satellite altimetry observations. It represents an improved version of the reconstruction by Church and White (2011), utilizing a reduced-space optimal interpolation technique. Enhancements include the incorporation of sea level fingerprints, sterodynamic sea level change patterns, and more comprehensive estimates of local vertical land motion.

### Data supplement for "Improved Sea Level Reconstruction from 1900 to 2019" by Jinping Wang, John A. Church, Xuebin Zhang, and Xianyao Chen.

Please email wangjinping@ouc.edu for questions.

### PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA ###Please cite “Improved Sea Level Reconstruction from 1900 to 2019” when using this data set.Our results rely on previous work, and please acknowledge the previous work by citing the original sources of the data. 

This data supplement contains the following files:GMSL_yr.nc & GMSL_yr.txtThe reconstructed global mean sea level time series from 1900 to 2019, along with the associated one standard deviation uncertainty.

Regional_yr.nc The regional sea level reconstruction fields from 1900 to 2019 at 1° spatial resolution.
DOI: 10.5281/zenodo.11030299
title: Integrated Effect Database for Toxicological Observations (INTOB)

description: This dataset contains morphological observations of zebrafish embryos from toxicological experiments from the department of Ecotoxicology at the Helmholtz-Center for Environmental Research (UFZ). Data was recorded via INTOB, a software to collect and manage data and metadata from toxicological observations.

The dataset contains two directories, data/which contains experimental data, and  scripts/ which contains all code needed to run the analysis described in the paper, as well as a compiled HTML file.

data/intob_data/ contains several csv files:



effect_list_info.csv contains all possible effects of the database and their relation to each other

obs_phenotypes.csv contains all individual observations

metadata.csv lists metadata about the experiments and substances

plate_layout.csv contains experimental setups, i.e. layouts of substances and concentrations in well-plates or vials

phys_chem_prop.csv contains the physico-chemical observations of the substances


data/data_for_analysis/ contains files required in the script and data/zotero_phenotype_library/ contains the results of the literature review described in the paper in JSON format.
DOI: 10.25919/jkj3-rs08
title: Long-term project observations for project P885 semester 2025APRS_02

description: We propose a modest program to continue monitoring 4 of the 6 known radio magnetars in order to achieve three primary science goals. The first is to characterise magnetar outbursts over long timescales, for which tracking their rotational, flux density, and polarisation properties provide a clear view of the impulse response of their magnetic fields. Second, understanding the links between magnetars the mysterious fast radio burst phenomenon through the discovery of rare emission and propagation effects, shared spectro-temporal phenomenology, and connections to high-energy (X-ray/gamma-ray) phenomena. Lastly, our continued monitoring has enormous benefit to the wider magnetar community, providing rapid alerts to changes in activity, adding context to unusual behaviour detected by high-enery observations, and a host of supplementary science through the teams extensive collaborative networks. The project and its precursors have been running since 2007 and have contributed to 21 publications since then. We are seeking to convert the project to long-term status, thereby also carrying these investigations into the SKA era.
DOI: 10.15151/esrf-es-2064081585
title: Redox control on the oxidation state of bridgmanite and CF phase in basaltic assemblages and the origin of retrograde bridgmanite inclusions

description: Experimental determination of phase equilibria at high pressure and high temperature is pivotal to interpreting geophysical and geochemical data that can help decipher the mineralogical composition of the Earth’s deep interior. Oxygen fugacity is one of the key variables affecting the stability of minerals at high pressure and temperature as it sets the oxidation state of Fe in a given mantle phase assemblages. We propose to use synchrotron Mössbauer spectroscopy to study the oxidation state of Fe in (Mg,Fe2+,Fe3+,Al)(Si,Al,Fe3+)O3 bridgmanite and CaFe2O4-type aluminous phase [(Na,Mg,Fe2+)(Si,Al,Fe3+)2O4] synthesized at conditions of the shallow lower mantle under controlled oxygen fugacity in a multi-anvil press. Results will help inform geophysical and geochemical observations and interpretations, from seismic anomalies associated with the presence of subducted oceanic lithologies in the lower mantle to the crystal chemistry of retrograde mineral inclusions in super-deep diamonds.
DOI: 10.5281/zenodo.15301882
title: Seismic Recordings of the 2025 M7.7 Myanmar Earthquake from the Southern Yunnan Array

description: The Southern Yunnan array is located 400–580 km from the epicenter of the 2025 M7.7 Myanmar Earthquake and comprising 60 broadband stations spaced ~25 km apart. This array is oriented nearly perpendicular to the strike of the Sagaing Fault, enhancing its sensitivity to along-fault rupture propagation.
DOI: 10.5281/zenodo.15301883
title: Seismic Recordings of the 2025 M7.7 Myanmar Earthquake from the Southern Yunnan Array

description: The Southern Yunnan array is located 400–580 km from the epicenter of the 2025 M7.7 Myanmar Earthquake and comprising 60 broadband stations spaced ~25 km apart. This array is oriented nearly perpendicular to the strike of the Sagaing Fault, enhancing its sensitivity to along-fault rupture propagation.
DOI: 10.57703/ce-6782p
title: Q81 raw data

description: Atomic Force Microscopy (AFM) topographic images of sample Q81 (raw data). Images were acquired in dynamic mode. We used Nanosensors PPP-FM silicon probes with a spring constant of 3 N/m, resonance frequency of 75 kHz and a tip radius of about 10 nm. Images were acquired with a free oscillation of about 30 nm and an amplitude reduction set point of 25% of the free amplitude. All samples were simply cleaned with a gentle stream of N2 gas.
DOI: 10.5281/zenodo.10071824
title: Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

description: Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.
DOI: 10.5281/zenodo.7983816
title: Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

description: Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.
DOI: 10.5281/zenodo.7983817
title: Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

description: Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of <em>in vitro</em> and <em>in silico</em> approaches for chemical testing and assessment.
DOI: 10.21227/hbje-ef33
title: "ITDAV-25 (Indian Thermal Dataset for Autonomous Vehicles)"

description: "ITDAV-25 (Indian Thermal Dataset for Autonomous Vehicles), a thermal image dataset specifically curated to advance research in Advanced Driver Assistance Systems (ADAS), particularly for environments characterized by low visibility, night-time conditions, and inclement weather. The dataset comprises of 13,688 raw thermal images, collected without any synthetic augmentation techniques. Out of these, 11,501 images are annotated in 2D (38292 number of instances), while the remaining 2,187 images are left unannotated to support tasks such as unsupervised learning or domain adaptation. The annotated portion is divided into 10,132 training images and 1,369 validation images. "
DOI: 10.15468/dl.ggdw87
title: Occurrence Download

description: A dataset containing 2073 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Plethodon ouachitae Dunn & Heinze, 1933)"
 ]
}

 The dataset includes 2073 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004268-250426092105405/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.14994954
title: Multivariate Temporal Point Process Regression

description: Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.
DOI: 10.6084/m9.figshare.14994954.v3
title: Multivariate Temporal Point Process Regression

description: Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.
DOI: 10.6084/m9.figshare.14994954.v2
title: Multivariate Temporal Point Process Regression

description: Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.
DOI: 10.6084/m9.figshare.16660887
title: Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

description: Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pretreatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
DOI: 10.6084/m9.figshare.16660887.v2
title: Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

description: Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pretreatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
DOI: 10.6084/m9.figshare.16660887.v1
title: Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

description: Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The paper presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pre-treatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
DOI: 10.6084/m9.figshare.16776056
title: Rerandomization in Stratified Randomized Experiments

description: Stratification and rerandomization are two well-known methods used in randomized experiments for balancing the baseline covariates. Renowned scholars in experimental design have recommended combining these two methods; however, limited studies have addressed the statistical properties of this combination. This article proposes two rerandomization methods to be used in stratified randomized experiments, based on the overall and stratum-specific Mahalanobis distances. The first method is applicable for nearly arbitrary numbers of strata, strata sizes, and stratum-specific proportions of the treated units. The second method, which is generally more efficient than the first method, is suitable for situations in which the number of strata is fixed with their sizes tending to infinity. Under the randomization inference framework, we obtain the asymptotic distributions of estimators used in these methods and the formulas of variance reduction when compared to stratified randomization. Our analysis does not require any modeling assumption regarding the potential outcomes. Moreover, we provide asymptotically conservative variance estimators and confidence intervals for the average treatment effect. The advantages of the proposed methods are exhibited through an extensive simulation study and a real-data example.