cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3153181.v5
title: Using Google Earth to improve management of threatened limestone karst ecosystems in Peninsular Malaysia

description: Biodiversity conservation is now about prioritisation, especially in a world with limited resources and so many habitats and species in need of protection. However, we cannot prioritise effectively if historical and current information on a particular habitat or species remains scattered. Several good platforms have been created to help users to find, use and create biodiversity information. However, good platforms for sharing habitat information for threatened ecosystems are still lacking. Limestone hills are an example of threatened ecosystems that harbour unique biodiversity, but are facing intensifying anthropogenic disturbances. As limestone is a vital resource for the construction industry, it is not possible to completely halt forest degradation and quarrying in developing countries such as Malaysia, where 445 limestone hills have been recorded in the peninsula to date. As such, there is an urgent need to identify which hills must be prioritised for conservation. To make decisions based on sound science, collating spatial and biological information on limestone hills into a publicly accessible database is critical. Here, we compile Malaysia’s first limestone hill GIS map for 445 limestone hills in the peninsula based on information from geological reports and scientific literature. To assist in conservation prioritisation efforts, we quantify characteristics of limestone hills in terms of size, degree of isolation, and spatial distribution patterns and also assessed the degree of habitat disturbance of each limestone hill in terms of buffer area forest degradation and quarrying activity. All this information is stored in a KMZ file and can be accessed through the Google Earth interface. This product should not be viewed as a final output containing basic limestone hill information. Rather, this database is a foundational platform for users to collect, store, update and manipulate spatial and biological data from limestone hills to better inform decisions regarding their management.
DOI: 10.6084/m9.figshare.3153082
title: Data from: A flexible, interpretable framework for assessing sensitivity to unmeasured confounding

description: When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees (BART) into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language.
DOI: 10.6084/m9.figshare.3151744
title: Data from: Likelihood Ratio and Score Tests to Test the Non-inferiority (or Equivalence) of the Odds Ratio in a Crossover Study with Binary Outcomes

description: We consider the non-inferiority (or equivalence) test of the odds ratio (OR) in a crossover study with binary outcomes to evaluate the treatment effects of two drugs. To solve this problem, Lui and Chang (2011) proposed both an asymptotic method and a conditional method based on a random effects logit model. Kenward and Jones (1987) proposed a likelihood ratio test (LRT_M) based on a log linear model. These existing methods are all subject to model misspecification. In this paper, we propose a likelihood ratio test (LRT) and a score test that are independent of model specification. Monte Carlo simulation studies show that, in scenarios considered in this paper, both the LRT and the score test have higher power than the asymptotic and conditional methods for the non-inferiority test; the LRT, score and asymptotic methods have similar power and they all have higher power than the conditional method for the equivalence test. When data can be well described by a log linear model, the LRT_M has the highest power among all the five methods (LRT_M, LRT, score, asymptotic and conditional) for both non-inferiority and equivalence tests. However, in scenarios for which a log linear model does not describe the data well, the LRT_M has the lowest power for the non-inferiority test and has inflated type I error rates for the equivalence test. We provide an example from a clinical trial that illustrates our methods.
DOI: 10.6084/m9.figshare.3153082.v1
title: Data from: A flexible, interpretable framework for assessing sensitivity to unmeasured confounding

description: When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees (BART) into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language.
DOI: 10.6084/m9.figshare.3153181.v6
title: Using Google Earth to improve management of threatened limestone karst ecosystems in Peninsular Malaysia

description: Biodiversity conservation is now about prioritisation, especially in a world with limited resources and so many habitats and species in need of protection. However, we cannot prioritise effectively if historical and current information on a particular habitat or species remains scattered. Several good platforms have been created to help users to find, use and create biodiversity information. However, good platforms for sharing habitat information for threatened ecosystems are still lacking. Limestone hills are an example of threatened ecosystems that harbour unique biodiversity, but are facing intensifying anthropogenic disturbances. As limestone is a vital resource for the construction industry, it is not possible to completely halt forest degradation and quarrying in developing countries such as Malaysia, where 445 limestone hills have been recorded in the peninsula to date. As such, there is an urgent need to identify which hills must be prioritised for conservation. To make decisions based on sound science, collating spatial and biological information on limestone hills into a publicly accessible database is critical. Here, we compile Malaysia’s first limestone hill GIS map for 445 limestone hills in the peninsula based on information from geological reports and scientific literature. To assist in conservation prioritisation efforts, we quantify characteristics of limestone hills in terms of size, degree of isolation, and spatial distribution patterns and also assessed the degree of habitat disturbance of each limestone hill in terms of buffer area forest degradation and quarrying activity. All this information is stored in a KMZ file and can be accessed through the Google Earth interface. This product should not be viewed as a final output containing basic limestone hill information. Rather, this database is a foundational platform for users to collect, store, update and manipulate spatial and biological data from limestone hills to better inform decisions regarding their management.
DOI: 10.6084/m9.figshare.3151744.v1
title: Data from: Likelihood Ratio and Score Tests to Test the Non-inferiority (or Equivalence) of the Odds Ratio in a Crossover Study with Binary Outcomes

description: We consider the non-inferiority (or equivalence) test of the odds ratio (OR) in a crossover study with binary outcomes to evaluate the treatment effects of two drugs. To solve this problem, Lui and Chang (2011) proposed both an asymptotic method and a conditional method based on a random effects logit model. Kenward and Jones (1987) proposed a likelihood ratio test (LRT_M) based on a log linear model. These existing methods are all subject to model misspecification. In this paper, we propose a likelihood ratio test (LRT) and a score test that are independent of model specification. Monte Carlo simulation studies show that, in scenarios considered in this paper, both the LRT and the score test have higher power than the asymptotic and conditional methods for the non-inferiority test; the LRT, score and asymptotic methods have similar power and they all have higher power than the conditional method for the equivalence test. When data can be well described by a log linear model, the LRT_M has the highest power among all the five methods (LRT_M, LRT, score, asymptotic and conditional) for both non-inferiority and equivalence tests. However, in scenarios for which a log linear model does not describe the data well, the LRT_M has the lowest power for the non-inferiority test and has inflated type I error rates for the equivalence test. We provide an example from a clinical trial that illustrates our methods.
DOI: 10.6084/m9.figshare.3146878
title: Wikipedia Navigation Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3153181.v7
title: Using Google Earth to improve management of threatened limestone karst ecosystems in Peninsular Malaysia

description: Biodiversity conservation is now about prioritisation, especially in a world with limited resources and so many habitats and species in need of protection. However, we cannot prioritise effectively if historical and current information on a particular habitat or species remains scattered. Several good platforms have been created to help users to find, use and create biodiversity information. However, good platforms for sharing habitat information for threatened ecosystems are still lacking. Limestone hills are an example of threatened ecosystems that harbour unique biodiversity, but are facing intensifying anthropogenic disturbances. As limestone is a vital resource for the construction industry, it is not possible to completely halt forest degradation and quarrying in developing countries such as Malaysia, where 445 limestone hills have been recorded in the peninsula to date. As such, there is an urgent need to identify which hills must be prioritised for conservation. To make decisions based on sound science, collating spatial and biological information on limestone hills into a publicly accessible database is critical. Here, we compile Malaysia’s first limestone hill GIS map for 445 limestone hills in the peninsula based on information from geological reports and scientific literature. To assist in conservation prioritisation efforts, we quantify characteristics of limestone hills in terms of size, degree of isolation, and spatial distribution patterns and also assessed the degree of habitat disturbance of each limestone hill in terms of buffer area forest degradation and quarrying activity. All this information is stored in a KMZ file and can be accessed through the Google Earth interface. This product should not be viewed as a final output containing basic limestone hill information. Rather, this database is a foundational platform for users to collect, store, update and manipulate spatial and biological data from limestone hills to better inform decisions regarding their management.
DOI: 10.6084/m9.figshare.3146842
title: A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature

description: These posters describe a use of information extraction and crowdsourcing to populate a database of polymer properties.<br>
DOI: 10.6084/m9.figshare.3153193.v6
title: CODE: Pre-processing

description: This repository contains the scripts used for doing part of the Quality Control (QC) results presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the QC.
DOI: 10.6084/m9.figshare.3146839
title: Up-right Microscopy

description: 正立顕微鏡
DOI: 10.6084/m9.figshare.3153181.v8
title: Using Google Earth to improve management of threatened limestone karst ecosystems in Peninsular Malaysia

description: Biodiversity conservation is now about prioritisation, especially in a world with limited resources and so many habitats and species in need of protection. However, we cannot prioritise effectively if historical and current information on a particular habitat or species remains scattered. Several good platforms have been created to help users to find, use and create biodiversity information. However, good platforms for sharing habitat information for threatened ecosystems are still lacking. Limestone hills are an example of threatened ecosystems that harbour unique biodiversity, but are facing intensifying anthropogenic disturbances. As limestone is a vital resource for the construction industry, it is not possible to completely halt forest degradation and quarrying in developing countries such as Malaysia, where 445 limestone hills have been recorded in the peninsula to date. As such, there is an urgent need to identify which hills must be prioritised for conservation. To make decisions based on sound science, collating spatial and biological information on limestone hills into a publicly accessible database is critical. Here, we compile Malaysia’s first limestone hill GIS map for 445 limestone hills in the peninsula based on information from geological reports and scientific literature. To assist in conservation prioritisation efforts, we quantify characteristics of limestone hills in terms of size, degree of isolation, and spatial distribution patterns and also assessed the degree of habitat disturbance of each limestone hill in terms of buffer area forest degradation and quarrying activity. All this information is stored in a KMZ file and can be accessed through the Google Earth interface. This product should not be viewed as a final output containing basic limestone hill information. Rather, this database is a foundational platform for users to collect, store, update and manipulate spatial and biological data from limestone hills to better inform decisions regarding their management.
DOI: 10.6084/m9.figshare.3153193.v7
title: CODE: Pre-processing

description: This repository contains the scripts used for doing part of the Quality Control (QC) results presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the QC.
DOI: 10.6084/m9.figshare.3146842.v1
title: CHIMaD-Poster-Roselyne-2015.pptx

description: These posters describe a use of information extraction and crowdsourcing to populate a database of polymer properties.<br>
DOI: 10.6084/m9.figshare.3146878.v1
title: Wikipedia Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3146842.v3
title: A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature

description: These posters describe a use of information extraction and crowdsourcing to populate a database of polymer properties.<br>
DOI: 10.6084/m9.figshare.3146797
title: What is a complex innovation system - Intermediate Data

description: What is a complex innovation system?<br>Intermediate data for Tables 1 & 2
DOI: 10.6084/m9.figshare.3146842.v2
title: A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature

description: These posters describe a use of information extraction and crowdsourcing to populate a database of polymer properties.<br>
DOI: 10.6084/m9.figshare.3146797.v1
title: What is a complex innovation system - Intermediate Data

description: What is a complex innovation system?<br>Intermediate data for Tables 1 & 2
DOI: 10.6084/m9.figshare.3153193.v8
title: CODE: Pre-processing

description: This repository contains the scripts used for doing part of the Quality Control (QC) results presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the QC.
DOI: 10.6084/m9.figshare.3146776
title: Beta-D-Ribofuranose

description: Geometry optimisation of B-D-Ribofuranose in water at the 6-31G(d) level of theory using the wB97X-D functional.<br>
DOI: 10.6084/m9.figshare.3146878.v2
title: Wikipedia Navigation Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3146749
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DEEs and DEGs]

description: This repository contains the scripts used to identify differentially expressed genes (DEGs) and differentially expressed exons (DEEs) in the reference below.<br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146776.v1
title: Beta-D-Ribofuranose

description: Geometry optimisation of B-D-Ribofuranose in water at the 6-31G(d) level of theory using the wB97X-D functional.<br>