cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60712/si-id254663.1
title: StrainInfo SI-ID 254663.1

description: StrainInfo dataset 254663 about a strain of Pleurotus ostreatus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254664.1
title: StrainInfo SI-ID 254664.1

description: StrainInfo dataset 254664 about a strain of Agaricus arvensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254665.1
title: StrainInfo SI-ID 254665.1

description: StrainInfo dataset 254665 about a strain of Agaricus arvensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254725.1
title: StrainInfo SI-ID 254725.1

description: StrainInfo dataset 254725 about a strain of Globisporangium paroecandrum. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254726.1
title: StrainInfo SI-ID 254726.1

description: StrainInfo dataset 254726 about a strain of Pythium vanterpoolii. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254727.1
title: StrainInfo SI-ID 254727.1

description: StrainInfo dataset 254727 about a strain of Calcarisporium arbuscula. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254666.1
title: StrainInfo SI-ID 254666.1

description: StrainInfo dataset 254666 about a strain of Agaricus arvensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254822.1
title: StrainInfo SI-ID 254822.1

description: StrainInfo dataset 254822 about a strain of Stagonospora tainanensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254667.1
title: StrainInfo SI-ID 254667.1

description: StrainInfo dataset 254667 about a strain of Cystobasidium minutum. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254668.1
title: StrainInfo SI-ID 254668.1

description: StrainInfo dataset 254668 about a strain of Naganishia albida. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254728.1
title: StrainInfo SI-ID 254728.1

description: StrainInfo dataset 254728 about a strain of Unidentified. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254729.1
title: StrainInfo SI-ID 254729.1

description: StrainInfo dataset 254729 about a strain of Hypomyces rosellus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254730.1
title: StrainInfo SI-ID 254730.1

description: StrainInfo dataset 254730 about a strain of Fusarium verticillioides. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254731.1
title: StrainInfo SI-ID 254731.1

description: StrainInfo dataset 254731 about a strain of Unidentified. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254823.1
title: StrainInfo SI-ID 254823.1

description: StrainInfo dataset 254823 about a strain of Stagonospora tainanensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254824.1
title: StrainInfo SI-ID 254824.1

description: StrainInfo dataset 254824 about a strain of Drepanopeziza brunnea. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254669.1
title: StrainInfo SI-ID 254669.1

description: StrainInfo dataset 254669 about a strain of Rhodotorula mucilaginosa. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254825.1
title: StrainInfo SI-ID 254825.1

description: StrainInfo dataset 254825 about a strain of Plenodomus tracheiphilus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id254670.1
title: StrainInfo SI-ID 254670.1

description: StrainInfo dataset 254670 about a strain of Debaryomyces hansenii. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.15468/dl.c822bf
title: Occurrence Download

description: A dataset containing 33969332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia",
 "Year 1000-2023"
 ]
}

 The dataset includes 33969332 records from 13366 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082643-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18502563
title: data and code
DOI: 10.15468/dl.cc8t9d
title: Occurrence Download

description: A dataset containing 279460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-85.5643 34.45811,-76.84288 34.37898,-76.82824 41.25393,-85.25119 41.32701,-85.5643 34.45811))",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June, July, August, September)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year is 2020"
 ]
}

 The dataset includes 279460 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015948-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jphqsz
title: Occurrence Download

description: A dataset containing 92147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mangifera indica"
 ]
}

 The dataset includes 92147 records from 596 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015909-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17207856
title: test code and data for "GEDI Waveform-Adaptive Metrics for Enhanced Canopy Height Retrieval in Dense Amazon Forests"

description: ==================================================Replication Package for:"GEDI Waveform-Adaptive Metrics for Enhanced Canopy Height Retrieval in Dense Amazon Forests"==================================================

AUTHORS-------Yianyu Yu, Wenjian Ni, Zhiyu Zhang, Zhifeng Guo, Jie Wang, Chuan Wang

DESCRIPTION-----------This repository contains the code and test data for the Waveform-Adaptive Elevation and height Detection (WAED) algorithm. This algorithm is designed to improve ground detection and canopy height retrieval from GEDI waveform data in very dense forest environments.

SYSTEM REQUIREMENTS-------------------- **Operating System**: Windows 10 or newer- **Software**: MATLAB R2018a or newer- **Required MATLAB Toolboxes**:    - Image Processing Toolbox    - Statistics and Machine Learning Toolbox    - (Optional) Parallel Computing Toolbox (for faster processing)

FILE STRUCTURE--------------- `main_WAED.m`: The main script of the WAED algorithm. It performs ground and canopy height extraction and assesses the results against ALS reference data.- `test_data/`: Directory containing the test data.    - Contains two tracks of GEDI data from two study areas.    - Includes GEDI L1B and L2A products, ALS reference data, study area boundaries, and NASADEM slope files.- `lib_function/`: Directory containing auxiliary functions required to support the main script.

QUICK START GUIDE-----------------1. Unzip `WAED_code_test_data.7z` to your desired working directory.2. Launch MATLAB and navigate to the unzipped directory in the MATLAB Command Window:

LICENSE-------This replication package is provided to support the peer review process of the associated manuscript. Full public release will occur upon article acceptance.

SUPPORT-------For technical questions regarding code implementation, please contact:- Yianyu Yu: yuty@radi.ac.cn- Wenjian Ni: niwj@aircas.ac.cn
DOI: 10.5281/zenodo.17207857
title: test code and data for "GEDI Waveform-Adaptive Metrics for Enhanced Canopy Height Retrieval in Dense Amazon Forests"

description: ==================================================Replication Package for:"GEDI Waveform-Adaptive Metrics for Enhanced Canopy Height Retrieval in Dense Amazon Forests"==================================================

AUTHORS-------Yianyu Yu, Wenjian Ni, Zhiyu Zhang, Zhifeng Guo, Jie Wang, Chuan Wang

DESCRIPTION-----------This repository contains the code and test data for the Waveform-Adaptive Elevation and height Detection (WAED) algorithm. This algorithm is designed to improve ground detection and canopy height retrieval from GEDI waveform data in very dense forest environments.

SYSTEM REQUIREMENTS-------------------- **Operating System**: Windows 10 or newer- **Software**: MATLAB R2018a or newer- **Required MATLAB Toolboxes**:    - Image Processing Toolbox    - Statistics and Machine Learning Toolbox    - (Optional) Parallel Computing Toolbox (for faster processing)

FILE STRUCTURE--------------- `main_WAED.m`: The main script of the WAED algorithm. It performs ground and canopy height extraction and assesses the results against ALS reference data.- `test_data/`: Directory containing the test data.    - Contains two tracks of GEDI data from two study areas.    - Includes GEDI L1B and L2A products, ALS reference data, study area boundaries, and NASADEM slope files.- `lib_function/`: Directory containing auxiliary functions required to support the main script.

QUICK START GUIDE-----------------1. Unzip `WAED_code_test_data.7z` to your desired working directory.2. Launch MATLAB and navigate to the unzipped directory in the MATLAB Command Window:

LICENSE-------This replication package is provided to support the peer review process of the associated manuscript. Full public release will occur upon article acceptance.

SUPPORT-------For technical questions regarding code implementation, please contact:- Yianyu Yu: yuty@radi.ac.cn- Wenjian Ni: niwj@aircas.ac.cn
DOI: 10.15468/dl.kwr93g
title: Occurrence Download

description: A dataset containing 26266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lissachatina fulica",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 26266 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015969-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18502078
title: SuperDARN data in netCDF format (2015-Jan)

description: 2015-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6434800
title: SuperDARN data in netCDF format (2015-Jan)

description: 2015-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.34740/kaggle/dsv/14750207
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.x974ca
title: Occurrence Download

description: A dataset containing 24186 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Enhydra lutris"
 ]
}

 The dataset includes 24186 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015970-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/aqsnby
title: Replication Data for: Gender, Performance, and Prize Money: A Study of Pay Discrimination Against Female Teams in eSports

description: The dataset contains match-tournament level information on professional Counter-Strike: Global Offensive (CS:GO) competitions, covering both female-only and male tournaments over the period 2022–2024. It includes event characteristics (event date, location, number of participating teams, total prize pool, and event overview), team-level outcomes (team identifier, final rank, prize money won, prize money from finals), and performance and exposure measures at the team–event level. Performance is captured by the average kill-to-death (KD) ratio, while audience attention is measured by the number of Twitch views. Gender is coded both as a categorical variable and a binary indicator (female = 1).
DOI: 10.6084/m9.figshare.28706308
title: Dose Selection Balancing Efficacy and Toxicity Using Bayesian Model Averaging

description: Successful pharmaceutical drug development requires identifying ranges of doses providing an acceptable efficacy response while not causing unacceptable intolerance or toxicity. This article describes a novel application of Bayesian model averaging (BMA) methodology to the construction of dose ranges with a high probability of clinical acceptability that can be read directly from simple graphical displays. The ranges are based on posterior distributions of efficacy and toxicity outcomes from possibly different unknown functional dose–response relationships and different likelihoods. The approach provides an operationally practical, graphically oriented, model agnostic strategy that is distributionally general, statistically valid, and allows for continuous, binary, or ordinal data. Efficacy and toxicity are considered in two ways: as separate outcomes for which possibly different dose–response functions are fitted along with simultaneous error bounds, and as outcomes generated by ordinal distributions of categories combining efficacy and toxicity nonlinearly. The calculations are carried out with readily available R software and require no additional programming. The performance of the method is evaluated via three simulation studies using efficacy and toxicity outcomes generated from copulas with different association patterns, and with application to a real clinical example. In all cases, the BMA strategy identified consistent ranges of acceptable doses.
DOI: 10.6084/m9.figshare.28706308.v1
title: Dose Selection Balancing Efficacy and Toxicity Using Bayesian Model Averaging

description: Successful pharmaceutical drug development requires identifying ranges of doses providing an acceptable efficacy response while not causing unacceptable intolerance or toxicity. This article describes a novel application of Bayesian model averaging (BMA) methodology to the construction of dose ranges with a high probability of clinical acceptability that can be read directly from simple graphical displays. The ranges are based on posterior distributions of efficacy and toxicity outcomes from possibly different unknown functional dose–response relationships and different likelihoods. The approach provides an operationally practical, graphically oriented, model agnostic strategy that is distributionally general, statistically valid, and allows for continuous, binary, or ordinal data. Efficacy and toxicity are considered in two ways: as separate outcomes for which possibly different dose–response functions are fitted along with simultaneous error bounds, and as outcomes generated by ordinal distributions of categories combining efficacy and toxicity nonlinearly. The calculations are carried out with readily available R software and require no additional programming. The performance of the method is evaluated via three simulation studies using efficacy and toxicity outcomes generated from copulas with different association patterns, and with application to a real clinical example. In all cases, the BMA strategy identified consistent ranges of acceptable doses.
DOI: 10.15468/dl.5ae66d
title: Occurrence Download

description: A dataset containing 32064 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Deroceras reticulatum",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 32064 records from 306 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015972-260129131611470/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.21967855
title: Dual-Orthogonal Arrays for Order-of-Addition Two-Level Factorial Experiments

description: A new class of orthogonal arrays called dual-orthogonal arrays is proposed in this paper to design order-of-addition two-level factorial experiments in which both component addition orders and component levels can be varied over treatments. Dual-orthogonal arrays can be viewed as an optimal combination of order-of-addition orthogonal arrays and two-level orthogonal arrays. Based on these two different concepts of orthogonality, when a compound model is used to fit the observed data, both pairwise order effects and component main effects can be estimated with optimal efficiency. Under the assumption of normality, these two kinds of parametric effects can also be inferred independently. A three-drug combination study is first used to show that dual-orthogonal arrays can be practical for real-world studies. Both combinatorial and computational methods are then introduced to construct dual-orthogonal arrays. Additionally, a design catalogue is generated for future work.
DOI: 10.6084/m9.figshare.21967855.v1
title: Dual-Orthogonal Arrays for Order-of-Addition Two-Level Factorial Experiments

description: A new class of orthogonal arrays called dual-orthogonal arrays is proposed in this paper to design order-of-addition two-level factorial experiments in which both component addition orders and component levels can be varied over treatments. Dual-orthogonal arrays can be viewed as an optimal combination of order-of-addition orthogonal arrays and two-level orthogonal arrays. Based on these two different concepts of orthogonality, when a compound model is used to fit the observed data, both pairwise order effects and component main effects can be estimated with optimal efficiency. Under the assumption of normality, these two kinds of parametric effects can also be inferred independently. A three-drug combination study is first used to show that dual-orthogonal arrays can be practical for real-world studies. Both combinatorial and computational methods are then introduced to construct dual-orthogonal arrays. Additionally, a design catalogue is generated for future work.