cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/pr00007772
title: Combined Histologic and Endoscopic Endpoints in Ulcerative Colitis

description: Ulcerative colitis (UC) is an inflammatory bowel disease that affects the colon and rectum. UC causes inflammation in the colon and ulcers. Resolution of symptoms is an important goal of treatment in UC, which is defined as clinical remission (CR). CR is assessed in clinical trials by using the partial Mayo score, with a score of 0 indicating CR (i.e. complete resolution of rectal bleeding and abdominal pain). In addition to CR, endoscopic disease is important to assess as patients who achieve endoscopic improvement, often defined as Mayo endoscopic subscore < 2, have a greater likelihood of favourable outcomes. UC patients who undergo endoscopy usually also have biopsies of the colon taken, which can be used to assess for histologic disease activity. Histologic assessment of the colon can provide important information regarding the degree of inflammation in the colon. The Geboes score is a histologic scoring tool frequently used to assess histologic disease in UC, with histologic improvement defined as Geboes highest grade < 3.2. Clinical trials of UC have adopted histologic endpoints as part of co-primary or secondary endpoints, such as histo-endoscopic mucosal improvement (HEMI), which is a combination of endoscopic improvement and histologic improvement.

Despite the growing importance of endoscopic and histologic outcomes in UC, it is unclear how combined definitions of endoscopic and histologic endpoints such as HEMI compare to stand alone definitions of endoscopic improvement and histologic improvement. The relevance of these endpoints compared to CR and corticosteroid-free CR also requires further investigation. Further, markers of clinical disease severity, as measured by the partial Mayo score, and markers of inflammatory burden need to be better understood.
DOI: 10.25934/pr00007817
title: Predictors of Histologic Response using Antibodies Against Interleukin-5 in Eosinophilic Esophagitis

description: Eosinophilic Esophagitis is a chronic immune condition of the esophagus characterized by inflammation and the presence of eosinophils (a type of white blood cell) in the esophagus[1]. Eosinophilia is defined as an elevated number of eosinophils which results in inflammation. The typical onset of this disease occurs in childhood, with symptoms that vary depending on the age of onset. Severity of disease is evaluated based on features from endoscopy and histologic findings from biopsies obtained from endoscopy [1]. Histology is the study of cells and tissue as they appear under a microscope. Treatments for eosinophilic esophagitis include topical steroids (used to treat many inflammatory conditions) and proton pump inhibitors (a group of medications that reduce acid production from cells that line the stomach), although few studies have been done prospectively in children. A recent study evaluated predictors of histologic response to steroid therapy among adult patients and identified baseline dilation was identified as the only predictor of histologic response to steroid treatment [2]. Interleukin-5 (IL-5) is a protein produced by the immune system which are known to play a key role in allergic conditions. Anti IL-5 monoclonal antibody drugs reduce the effect of IL-5. However, it is unclear what factors may influence histologic response to anti-IL-5 therapy in pediatric patients. Therefore, this study aims to evaluate baseline clinical, histologic, and endoscopic predictors of histologic response among pediatric patients using data from a clinical trial evaluating mepolizumab (an anti-IL-5) for the treatment of eosinophilic esophagitis.

Given the importance of histologic features of disease in eosinophilic esophagitis, patients may benefit from the knowledge generated from this study as it may optimize treatment and disease management. For example, esophagogastroduodenoscopy (EGD) is a type of endoscopic procedure that allows physicians to evaluate the esophagus, stomach and part of the small intestine. EGDs are an invasive procedure and understanding potential predictors of response could potentially reduce the frequency of this procedure.
DOI: 10.25934/pr00008325
title: Does scoring method impact estimation of significant individual changes using Patient-Reported Outcomes Measurement Information System (PROMIS) measures? Comparing Classical Test Theory versus Item Response Theory

description: The importance of collecting patient-reported outcome measures (PROMs) has been recognized in medical research and clinical trials. In clinical research and care, researchers and clinicians have increasingly used PROMs to understand individual changes responding to treatment over time in patient-reported health-related quality of life (HRQoL) outcomes in order to evaluate treatment effectiveness for individual patients.
Many PROMs rely on raw summed scores of item response as scales scores, following a classical test theory (CTT) framework. In recent decades, item response theory (IRT), which originates from a modern measurement framework, has gained popularity as an alternative to simple summation of scores. IRT has the ability to capture different response patterns and to address skewness of  population distribution that cannot be easily solved by using CTT. 
Although advantages and weaknesses of Classical Test Theory (CTT) and Item Response Theory (IRT) scores have been discussed thoroughly in previous literature, few studies have examined whether the two frameworks differ in their ability to detect individual change over time using observed (empirical) data. This study will provide evidence-based guidance in detecting individual changes based on CTT and IRT scores under various measurement conditions and leads to recommendations for identifying responders to treatment for participants in clinical trials.
We will use a modelled and a clinical trial dataset to examine the ability of classical test theory (CTT) and item response theory (IRT) scores in identifying significant individual changes. A modeled dataset refers to simulation data, which are not collected from subjects. The clinical trial dataset refers to the STAR trial data. We will use both modeled/simulated data and the STAR trial dataset in this study. Classical Test Theory calculates a raw summed score of the scale scores for a measure. For this reason, each item is accounted for equally in the scale score. Item Response Theory estimates item parameters of each item and calculates the scale score for a measure. By doing so, each item can be assigned with different weights and contributes differently to the scale score. IRT has the ability to capture different response patterns and address the skewness of population distribution, which cannot be easily solved by using CTT. IRT has been applied in the construction and validation of several patient-reported outcome measures. This study further examines the benefits of IRT in terms of its capability of identifying responders to treatments, which is important to all clinical trials. By doing so, it would provide more confidence for researchers and practitioners to apply IRT rather than CTT because IRT can provide more information in terms of correctly identifying more responders to treatment.
DOI: 10.6084/m9.figshare.24312577
title: <b>Early human selection of crops' wild progenitors explains the acquisitive physiology of modern cultivars</b>

description: The "DomesticationPhysion" folder consists of two subfolders:The "rawData" subfolder contains all the input (raw) data of the <i>experimental dataset</i>. This includes leaf‐level measurements of gas exchange, morphology and chemistry of each individual plant included in the experiment. The "compiledData" subfolder contains species-specific data on diverse life-history traits (woodiness, growth form, life cycle, and photosynthetic pathway), taken from different global databases and published literature.
DOI: 10.6084/m9.figshare.24312577.v1
title: <b>Early human selection of crops' wild progenitors explains the acquisitive physiology of modern cultivars</b>

description: The "physio.xlsx" document contains all the raw data of the <i>experimental dataset</i>. This includes leaf‐level measurements of gas exchange, morphology and chemistry of each individual plant.
DOI: 10.6084/m9.figshare.22677598
title: Cereja River, Bragança, Pará, Brazil: water, sediment and benthic macroinvertebrate survey data

description: The impact of increasing urbanization was assessed in the Cereja River, eastern Amazonia in June and July 2021 using a Riparian Habitat Diversity Index (RHDI) (RHDI_en.csv), benthic macroinvertebrate structure (fauna_atualizada.csv) and water (agua.csv) and sediment (sedimento.csv) variables in ten areas along the Cereja using multivariate analyzes. Increasing urbanization is associated with lower RHDI, abundance, diversity and equity of benthic macroinvertebrates, higher conductivity, pH, temperature, width and percentage gravel.
DOI: 10.6084/m9.figshare.22677598.v1
title: Cereja River, Bragança, Pará, Brazil: water, sediment and benthic macroinvertebrate survey data

description: The impact of increasing urbanization was assessed in the Cereja River, eastern Amazonia in June and July 2021 using a Riparian Habitat Diversity Index (RHDI) (RHDI_en.csv), benthic macroinvertebrate structure (fauna_atualizada.csv) and water (agua.csv) and sediment (sedimento.csv) variables in ten areas along the Cereja using multivariate analyzes. Increasing urbanization is associated with lower RHDI, abundance, diversity and equity of benthic macroinvertebrates, higher conductivity, pH, temperature, width and percentage gravel.
DOI: 10.6084/m9.figshare.22656163.v1
title: Chapter1 Introduction

description: The following are metadata collected from the literature review for my PhD thesis work, which i plan further to put forward as a review article.
DOI: 10.6084/m9.figshare.22656163
title: Chapter1 Introduction

description: The following are metadata collected from the literature review for my PhD thesis work, which i plan further to put forward as a review article.
DOI: 10.5281/zenodo.10020177
title: The role of continental alkaline magmatism in mantle carbon outflux constrained by a machine learning analysis of zircon
DOI: 10.57841/casdc.0002913
title: 秸秆高浓度厌氧发酵技术数据集
DOI: 10.5281/zenodo.10020178
title: The role of continental alkaline magmatism in mantle carbon outflux constrained by a machine learning analysis of zircon
DOI: 10.6084/m9.figshare.24354736
title: <i>Phrynocephalus erythrurus</i> in Xizang

description: This data set contains the morphological traits of <i>Phrynocephalus erythrurus</i> sampled in Xizang, China. There is also microhabitat data of the trasects.
DOI: 10.6084/m9.figshare.24354736.v1
title: <i>Phrynocephalus erythrurus</i> in Xizang

description: This data set contains the morphological traits of <i>Phrynocephalus erythrurus</i> sampled in Xizang, China. There is also microhabitat data of the trasects.
DOI: 10.5281/zenodo.10020210
title: Raw Data for PALTIDE App

description: Raw data used to build PALTIDE app - more info can be found at Github Repo: https://github.com/keatonwilson/paleotidal_app
DOI: 10.5281/zenodo.10020211
title: Raw Data for PALTIDE App

description: Raw data used to build PALTIDE app - more info can be found at Github Repo: https://github.com/keatonwilson/paleotidal_app
DOI: 10.5281/zenodo.10020216
title: Deep underground seismic velocity changes in Sichuan associated with the 2013 Lushan earthquake

description: The GNSS data is provided by GNSS data product service platform of China Earthquake Administration. 
The data catalog is available at the website: https://data.earthquake.cn/datashare/report.shtml?PAGEID=earthquake_zhengshi. The data used in the manuscript is also uploaded.
DOI: 10.5281/zenodo.10020217
title: Deep underground seismic velocity changes in Sichuan associated with the 2013 Lushan earthquake

description: The GNSS data is provided by GNSS data product service platform of China Earthquake Administration. 
The data catalog is available at the website: https://data.earthquake.cn/datashare/report.shtml?PAGEID=earthquake_zhengshi. The data used in the manuscript is also uploaded.
DOI: 10.6084/m9.figshare.24331156
title: Research data

description: This is the data used to support the results presented in my paper, including pictures.This data is only used by journal editors to review this manuscripts.
DOI: 10.15468/9qssf9
title: Rich and underreported: First integrated assessment of the diversity of mesopelagic fishes in the Southwestern Tropical Atlantic

description: Here, we provide the first integrative study of the biodiversity of mesopelagic fishes of the southwestern Tropical Atlantic (SWTA), based on two expeditions in northeastern Brazil in 2015 and 2017. A full list of mesopelagic fishes of the region is provided, including rare species and new records for the Brazilian Exclusive Economic Zone and the indication of potentially new species in groups such as the Stomiiformes and Beryciformes.
DOI: 10.6084/m9.figshare.24354781
title: Data-Hg and C isotopes.xlsx

description: Data from Naqing, Palougou and Qiaotou sections
DOI: 10.6084/m9.figshare.24354781.v1
title: Data-Hg and C isotopes.xlsx

description: Data from Naqing, Palougou and Qiaotou sections
DOI: 10.17608/k6.auckland.24354778
title: Master Data SFR

description: The global loss of coastal habitats has significantly reduced the availability of intact ecosystems to serve as reference systems for marine restoration and conservation, posing a challenge for establishing ecological baselines to evaluate restoration and conservation success. In the Hauraki Gulf of New Zealand, extensive subtidal mussel reefs once played a critical role in maintaining the health of the system. However, due to large-scale exploitation, degradation of the surrounding ecosystem, and a lack of protection, these reefs have failed to recover naturally, leaving only a few remnant subtidal reefs at risk of further exploitation. This study compared mussel reef structure and large mobile species assemblages at natural remnant and restored mussel reefs in the Hauraki Gulf to evaluate their potential as reference sites for restoration and conservation efforts. Remnant and restored mussel reefs exhibited similar structure, however the proximity of remnant reefs to complex rocky-reef habitats resulted in higher mobile species diversity and abundance, facilitating a wider range of ecological interactions. This study supports the use of remnant reefs to assist with the evaluation of mussel reef restoration, which can help recover some of the lost biodiversity and underscores the importance of conserving the remaining mussel reefs in the region.
DOI: 10.17608/k6.auckland.24354778.v1
title: Master Data SFR

description: The global loss of coastal habitats has significantly reduced the availability of intact ecosystems to serve as reference systems for marine restoration and conservation, posing a challenge for establishing ecological baselines to evaluate restoration and conservation success. In the Hauraki Gulf of New Zealand, extensive subtidal mussel reefs once played a critical role in maintaining the health of the system. However, due to large-scale exploitation, degradation of the surrounding ecosystem, and a lack of protection, these reefs have failed to recover naturally, leaving only a few remnant subtidal reefs at risk of further exploitation. This study compared mussel reef structure and large mobile species assemblages at natural remnant and restored mussel reefs in the Hauraki Gulf to evaluate their potential as reference sites for restoration and conservation efforts. Remnant and restored mussel reefs exhibited similar structure, however the proximity of remnant reefs to complex rocky-reef habitats resulted in higher mobile species diversity and abundance, facilitating a wider range of ecological interactions. This study supports the use of remnant reefs to assist with the evaluation of mussel reef restoration, which can help recover some of the lost biodiversity and underscores the importance of conserving the remaining mussel reefs in the region.
DOI: 10.34740/kaggle/dsv/6735286
title: Our World in Data - COVID-19
DOI: 10.25345/c5gh9bm0n
title: MassIVE MSV000093148 - GNPS - Dataset Creation from GNPS Molcular Networking - 26bfbaebdd7245c182579769ee258822
title: Environmental extracts and fractions obtained with passive sampling (HP-20, SMIRC) from Mission Bay, Point Loma, Scripps Pier, Salton Sea (San Diego and Imperial Counties)
DOI: 10.13139/olcf/2004951
title: Tokenized Data for FORGE Foundation Models

description: This dataset comprises a vast corpus of 257 billion tokens, accompanied by the corresponding vocabulary file employed in the pre-training of FORGE foundation models. The primary data source for this corpus is scientific documents derived from diverse origins, and they have been tokenized using the Hugging Face BPE tokenizer. Further details about this research can be found in the publication titled "FORGE: Pre-Training Open Foundation Models for Science" authored by Junqi Yin, Sajal Dash, Feiyi Wang, and Mallikarjun (Arjun) Shankar, presented at SC'23. The data tokenization pipeline and resulting artifacts use CORE data [Ref: Knoth, P., & Zdrahal, Z. (2012). CORE: three access levels to underpin open access. D-Lib Magazine, 18(11/12)]. For use of these data sets for any purpose, please follow the guidelines provided in https://core.ac.uk/terms .
DOI: 10.25582/data.2022-08.2587403/2083537
title: Data for EMSL Project 51240 from August 2022
DOI: 10.25582/data.2021-10.2371387/2092204
title: Data for EMSL Project 51241 from October 2021
DOI: 10.25582/data.2021-09.2342591/2094721
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2342157/2094970
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339662/2096423
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340491/2096424
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340353/2096425
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340560/2096426
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340958/2096427
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340640/2096428
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340703/2096429
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2341405/2096430
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340552/2096431
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340562/2096432
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2341109/2096433
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340853/2096434
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340877/2096436
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340537/2096437
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339448/2096438
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339930/2096439
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340463/2096440
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340467/2096441
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2341408/2096443
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2341063/2096444
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339446/2096445
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340567/2096446
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340396/2096447
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340706/2096448
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339888/2096449
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2339540/2096450
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2320838/2096866
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2321320/2096867
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2327160/2096868
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2321161/2096869
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2321813/2097475
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316068/2100275
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315900/2100276
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317994/2100277
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315842/2100278
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315845/2100279
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316248/2100280
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316354/2100281
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315967/2100282
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316210/2100283
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316085/2100284
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318039/2100285
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315875/2100286
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315916/2100287
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316313/2100288
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317310/2100289
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317949/2100290
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315892/2100291
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316121/2100292
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2318089/2100293
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315835/2100294
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315938/2100295
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316211/2100296
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316593/2100297
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2317894/2100298
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315905/2100299
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315680/2100300
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315836/2100301
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316028/2100302
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315968/2100303
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315907/2100304
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315910/2100305
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316291/2100306
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2316639/2100307
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2315915/2100308
title: Data for EMSL Project 51241 from September 2021