cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6539087
title: Finding Reliable Sources: Evaluation Datasets

description: <strong>Finding Reliable Sources</strong> (<strong>FRS</strong>) is an NLP/ML task, in which, given a single-sentence claim, we aim to automatically find reliable sources that could be used to confirm or refute it. This repository includes evaluation datasets that could be used to train and test solution for this problem. Datasets are organised as large collections of individual records, each consisting of (1) a claim expressed a short text fragment and (2) a list of identifiers (DOI, ISBN, arXiv ID or URL) of reliable sources associated with it. Two datasets are included: Claim-Source Pairing Dataset (CSP) contains 32 million textual contexts paired with 24 million source identifiers mined from English Wikipedia, FEVER-FRS contains 15,798 claims gathered for the FEVER shared task, supplemented with source identifiers using Wikipedia. The data were gathered within a study described in the article <em>"Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach"</em>, presented at the 2022 International Joint Conference on Neural Networks (IJCNN 2022). Please refer to the article (in conference proceedings or authors' version) for more information on the FRS task, data collection procedures and evaluation results. The research was done within the HOMADOS project at the Institute of Computer Science, Polish Academy of Sciences. The datasets described here are based on the Wikipedia Complete Citation Corpus (WCCC), which also includes the train/test split for CSP. The CSP dataset is uploaded in three variants, using different text fragments as 'context'. In '<strong>s</strong>', the context consists of a single sentence preceding (or containing) a citation. The '<strong>ts</strong>' variant additionally includes a title of the Wikipedia article, while '<strong>ss</strong>' contains two sentences preceding a citation. Each variant contains subsequently numbered TSV files, including lines with the following values: document ID (see WCCC for full document metadata), textual context, a list of (at least one, possibly many) source identifiers. The FEVER-FRS dataset was created based on the FEVER shared task data. In the original dataset, each claim is matched with a set of 'evidence' sentences from Wikipedia that either support or refute it. For the purpose of evaluating FRS solutions, we replaced each evidence sentence with the identifiers to external sources that are cited alongside it. This was not always possible (e.g. for Wikipedia sentences with no sources, see the paper for details), but we managed to find 4414 and 11384 claims that are, respectively, refuted or supported by the provided sources. Each line of the FEVER-FRS TSV files contains the following: text of a short claim, a list of (at least one, possibly many) source identifiers. The source code code for generating both datasets (CSP from Wikipedia and FEVER-FRS from FEVER) can be obtained from a GitHub repository.
DOI: 10.5281/zenodo.6539086
title: Finding Reliable Sources: Evaluation Datasets

description: <strong>Finding Reliable Sources</strong> (<strong>FRS</strong>) is an NLP/ML task, in which, given a single-sentence claim, we aim to automatically find reliable sources that could be used to confirm or refute it. This repository includes evaluation datasets that could be used to train and test solution for this problem. Datasets are organised as large collections of individual records, each consisting of (1) a claim expressed a short text fragment and (2) a list of identifiers (DOI, ISBN, arXiv ID or URL) of reliable sources associated with it. Two datasets are included: Claim-Source Pairing Dataset (CSP) contains 32 million textual contexts paired with 24 million source identifiers mined from English Wikipedia, FEVER-FRS contains 15,798 claims gathered for the FEVER shared task, supplemented with source identifiers using Wikipedia. The data were gathered within a study described in the article <em>"Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach"</em>, presented at the 2022 International Joint Conference on Neural Networks (IJCNN 2022). Please refer to the article (in conference proceedings or authors' version) for more information on the FRS task, data collection procedures and evaluation results. The research was done within the HOMADOS project at the Institute of Computer Science, Polish Academy of Sciences. The datasets described here are based on the Wikipedia Complete Citation Corpus (WCCC), which also includes the train/test split for CSP. The CSP dataset is uploaded in three variants, using different text fragments as 'context'. In '<strong>s</strong>', the context consists of a single sentence preceding (or containing) a citation. The '<strong>ts</strong>' variant additionally includes a title of the Wikipedia article, while '<strong>ss</strong>' contains two sentences preceding a citation. Each variant contains subsequently numbered TSV files, including lines with the following values: document ID (see WCCC for full document metadata), textual context, a list of (at least one, possibly many) source identifiers. The FEVER-FRS dataset was created based on the FEVER shared task data. In the original dataset, each claim is matched with a set of 'evidence' sentences from Wikipedia that either support or refute it. For the purpose of evaluating FRS solutions, we replaced each evidence sentence with the identifiers to external sources that are cited alongside it. This was not always possible (e.g. for Wikipedia sentences with no sources, see the paper for details), but we managed to find 4414 and 11384 claims that are, respectively, refuted or supported by the provided sources. Each line of the FEVER-FRS TSV files contains the following: text of a short claim, a list of (at least one, possibly many) source identifiers. The source code code for generating both datasets (CSP from Wikipedia and FEVER-FRS from FEVER) can be obtained from a GitHub repository.
DOI: 10.6084/m9.figshare.21262020
title: Supplementary Table 1

description: This table describes the content of the Caulimoviridae genome library used as a query in CAULIFINDER Branch A. This is the original version published with the Mobile DNA paper (Oct 2022).
DOI: 10.6084/m9.figshare.21262020.v1
title: Supplementary table 1

description: This table describes the content of the Caulimoviridae genome library used as a query in CAULIFINDER Branch A. This is the original version published with the Mobile DNA paper (Oct 2022).
DOI: 10.6084/m9.figshare.21262026
title: Optimum soil parameters for macadamia production in Malawi

description: This table provides information on recommended soil parameters for healthy growth of macadamia trees especially for Malawi context
DOI: 10.6084/m9.figshare.21262026.v1
title: Optimum soil parameters for macadamia production in Malawi

description: This table provides information on recommended soil parameters for healthy growth of macadamia trees especially for Malawi context
DOI: 10.14469/hpc/10782
title: 4,5-dibromo-2,3,6,7-tetramethoxy-9,10-dihydrophenanthrene

description: Crystal structure data
DOI: 10.14469/hpc/10778
title: 4,5-difluoro-2,3,6,7-tetramethoxyphenanthrene

description: Crystal structure
DOI: 10.14469/hpc/10780
title: 1,2-bis(3-fluoro-4,5-dimethoxyphenyl)ethane

description: Crystal structure data
DOI: 10.14469/hpc/10787
title: 2,3,6,7-tetramethoxy-4,5-dipropyl-9,10-dihydrophenanthrene

description: Crystal structure data
DOI: 10.14469/hpc/10779
title: 4,5-difluoro-2,3,6,7-tetramethoxy-9,10-dihydrophenanthrene

description: Crystal structure data
DOI: 10.14469/hpc/10781
title: 4,4'-(ethane-1,2-diyl)bis(2-chloro-6-methoxyphenol)

description: Crystal structure data
DOI: 10.14469/hpc/10783
title: 4,5-dichloro-2,3,6,7-tetramethoxy-9,10-dihydrophenanthrene

description: Crystal structure data
DOI: 10.17632/hs34v2pc2h.1
title: Corneal transplants Katowice

description: Data for the corneal transplants conducted in Katowice.
DOI: 10.17632/hs34v2pc2h.2
title: Corneal transplants Katowice

description: Data for the corneal transplants conducted in Katowice.
DOI: 10.17632/89z4nts2zr.1
title: Does Financial Inclusion and Human Development progress sustainably? Evidence from Emerging countries

description: We employed financial inclusion (FI) as an index derived using principal component analysis (PCA) from 4 financial inclusion indicators: banking institutions, commercial bank branches, ATMs, and deposit accounts with commercial banks. Similarly, this study used the education index and health index formulated by PCA, and these two indices together proxied for human development. The education index was derived from primary school enrolment, secondary school enrolment, and internet users per 100 people. The health index was represented by current health expenditure per capita, domestic government health expenditure per capita, and domestic private health expenditure per capita. Control variables of the study include GDP per capita (Constant 2015 US$) was used to represent economic growth, CO2 emissions per capita and natural resources as a percentage of the GDP, and Remittances received as a percentage of GDP. Table III presents the list of variables and its sources.
DOI: 10.17632/jpdch96s95.1
title: Pre-operative geriatric screening and assessment as predictors of postoperative complications in elderly patients with gynecologic cancer: A pilot cohort study on a neglected issue

description: A BS T R A C T
Objectives: Ovarian and uterine malignancies in the elderly are a neglected issue in literature and preoperative assessment is often challenging for gynecologic oncologists. We aimed to investigate if Comprehensive Geriatric Assessment (CGA) is associated with major post-operative complications in this specific setting.
Methods: Patients 65 years or older with gynecologic cancers undergoing CGA before cytoreductive surgery were eligible. CGA scores, Geriatric 8 (G8), Performance Status (PS), American Society of Anesthesiologists (ASA) as well as Surgical Complexity Score (SCS) were evaluated for association with 30-day postoperative complications (POCs) Clavien-Dindo ≥ 2. Multivariate regression analyses were conducted.
Results: Grade 2 and grade 3 POCs developed in 10 and 6 patients (18.2% and 10.9%), hemorrhagic complications being the most common. R0 cytoreduction was completed in 83.6% cases, 20 and 5 surgeries were completed respectively with laparoscopic and robotic approach. Despite high prevalence of ASA3, most of patients were ADL and IADL independent, showed good physical and cognitive performance and had no severe comorbidities. Traditional variables (age, FIGO stage, PS, ASA, SCS) were not significantly associated with outcome. Only G8 ≤14 survived multivariate analysis as an independent predictor of complications.
Conclusions: If adequately selected, gynecologic cancer surgery in the elderly can be radical, safe and minimally invasive. Even if CGA didn’t show significative association with POCs, it offered a different and wider assessment than anesthesiologic evaluation. G8 could be a surgical complication predictor.
DOI: 10.5281/zenodo.7133246
title: Pseudomonas aeruginosa Centrifuge DB

description: Pseudomonas aeruginosa Plascope DB
DOI: 10.17632/36fjrg9s69.2
title: Dataset for COVID-19 segmentation and severity scoring

description: In this dataset, we present a set of chest X-ray images of COVID-19 positive and negative patients, with the aim of performing segmentation and scoring of disease-affected areas in the lungs. The total number of images is 1,364. Of them, 580 are COVID-19 positive images (43%) and 784 images show no findings (57%). Each COVID-19 image contains the segmentation mask of the COVID-19-affected area and the associated severity score.

We collected and pre-processed four publicly available datasets used for the classification of COVID-19 and pneumonia, namely:
•	Actualmed COVID-19 Chest X-ray Dataset containing 49 images of COVID-19 patients [1];
•	COVID-19 Radiography Database containing 104 images of COVID-19 patients [2];
•	COVID Chest X-Ray Dataset  containing 399 images of COVID-19 patients [3];
•	Figure1 COVID Chest X-ray Dataset containing 28 images of COVID-19 patients [4].

Then, two senior radiologists in our team, from the United States and Russia, annotated anteroposterior and posteroanterior radiographs. Each annotator has more than 10 years of experience and has been working extensively during the COVID-19 pandemic. The annotators labeled the datasets independently. Despite being time-consuming, this allowed us to get a consensus severity score of COVID-19-positive patients. Each patient has been labeled on a scale from 0 to 6, where 0 means no findings, while 6 is a severe form of COVID-19 which affects more than 85% of the lungs.

In addition to COVID-19 data, we also used two datasets with normal lungs without any pneumonia or any other abnormalities. Those healthy lung images have also been validated by the same two radiologists. Those images are represented by two datasets, namely:
•	Chest X-ray Normal Dataset containing 431 images of patients with no findings [5];
•	RSNA Normal Dataset containing 353 images of patients with no findings [6].

References:
1. 	Wang L, Wong A, Qiu ZL, McInnis P, Chung A, Gunraj H, et al. Actualmed COVID-19 Chest X-ray Dataset Initiative. 2020. Available: https://github.com/agchung/Actualmed-COVID-chestxray-dataset
2. 	COVID-19 Radiography Database. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
3. 	COVID-19 Image Data Collection. 2020. Available: https://github.com/ieee8023/covid-chestxray-dataset
4. 	Wang L, Wong A, Qiu ZL, McInnis P, Chung A, Gunraj H, et al. Figure 1 COVID-19 Chest X-ray Dataset Initiative. 2020. Available: https://github.com/agchung/Figure1-COVID-chestxray-dataset
5. 	Chest X-Ray Images (Pneumonia). Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
6. 	RSNA Pneumonia Detection Challenge. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
DOI: 10.17632/8gx4p2x5vw.1
title: The row data  presented in Data of combination effect of atovaquone, mefloquine and 3-bromopyruvic acid against Echinococcus multilocularis protoscoleces.

description: The dataset presented here is related to a previous research article titled “Mitochondrial Complex III in Larval Stage of Echinococcus multilocularis as a Potential Chemotherapeutic Target and in vivo Efficacy of Atovaquone Against Primary Hydatid Cysts”[1]. In this report, data were collected by aerobic and anaerobic culture assay of E. multilocularis protscoleces in the presence of 3 anti-echinococcal drug candidates (atovaquone, mefloquine and 3-bromopropionic acid). The data were analyzed by viability of the protoscoleces between day 0 to day 7 from adding drug candidates. In aerobic condition, all drug candidates gave damage to the protoscoleces as described previously [1, 2, 3, 4]. Mefroquine alone and combination use of atovaquone with mefloquine immediately eliminated the protoscoleces while combination of atovaquone with 3-bromopyruvic acid did not showed clear synergy effect. In anaerobic condition, mefloquine alone and combination of atovaquone with mefloquine eliminated protoscoleces immadiately. 3-bromopyruvic acid showed stronger efficacy than that of aerobic condition. Furthermore, combination of atvaquone with 3-bromopruvic acid eliminated the protoscoleces, indicated synergy effect appeared under only anaerobic condition. The data clarified combination use of these three drug eliminated protoscoleces in both aerobic- and anaerobic conditon, which suggested that these could inhibit Echinococcus multilocularis aerobic and anaerobic respiration pathways in vivo. The data would be useful for the development of new drug dosing method for alveolar echinococosis.
DOI: 10.5281/zenodo.6851211
title: Pseudomonas aeruginosa Centrifuge DB

description: Pseudomonas aeruginosa Plascope DB
DOI: 10.17632/d2yn52n9c9.1
title: Dataset: Improved Benthic Classification from Different AUV Surveys with Unsupervised Domain Adaptation

description: This dataset contains patch images of benthic species and physical features from two sets of AUV surveys used for machine learning research into Unsupervised Domain Adaptation.  The dataset includes a README.md to explain the structure of the directories.

The first dataset (EMR) is from Elizabeth and Middleton Reef, Australia captured in January 2020 by the AUV Sirius and the AUV Nimbus.  The second dataset (SHR) is from the South Hydrate Ridge in the North Eastern Pacific captured by the AUV AE2000F and the AUV Tuna-sand during the Adaptive Robotics cruise in 2018.  

This dataset is intended for machine learning research.  The original images and manual point annotations are available on squidle.org (EMR) and soi.squidle.org (SHR).  

ACKNOWLEDGMENT
The data from the EMR dataset was part of Australia’s Integrated Marine Observing System (IMOS) – IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS).  The EMR annotations were based on work undertaken for the Marine Biodiversity Hub, a collaborative partnership supported through funding from the Australian Government’s National Environmental Science Program (NESP).  The images for the SHR dataset were collected during the Schmidt Ocean Institute's FK180731 \#Adaptive Robotics campaign, with support from the Japanese Government's Zipangu in the Ocean Strategic Innovation Program.
DOI: 10.17632/d2yn52n9c9.2
title: Dataset: Improved Benthic Classification from Different AUV Surveys with Unsupervised Domain Adaptation

description: This dataset contains patch images of benthic species and physical features from two sets of AUV surveys used for machine learning research into Unsupervised Domain Adaptation.  The dataset includes a README.md explaining the structure of the directories and samples of the image patches by class.

The first dataset (EMR) is from Elizabeth and Middleton Reef, Australia captured in January 2020 by the AUV Sirius and the AUV Nimbus including 2658 image patches with 6 classes (5 benthic species and one physical feature).  The second dataset (SHR) is from the South Hydrate Ridge in the North Eastern Pacific captured by the AUV AE2000F and the AUV Tuna-sand during the Adaptive Robotics cruise in 2018 including 949 image patches with 8 classes (5 benthic species and 3 physical features).  

This dataset is intended for machine learning research.  The original images and manual point annotations are available on squidle.org (EMR) and soi.squidle.org (SHR).  See 'Steps to reproduce' for more details.

ACKNOWLEDGMENT
The data from the EMR dataset was part of Australia’s Integrated Marine Observing System (IMOS) – IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS).  The EMR annotations were based on work undertaken for the Marine Biodiversity Hub, a collaborative partnership supported through funding from the Australian Government’s National Environmental Science Program (NESP).  The images for the SHR dataset were collected during the Schmidt Ocean Institute's FK180731 Adaptive Robotics campaign, with support from the Japanese Government's Zipangu in the Ocean Strategic Innovation Program.
DOI: 10.3886/e180181v1
title: Is Teacher Effectiveness Stable Across School Contexts? An Examination of Teachers who Transfer into Turnaround Schools

description: Turnaround interventions often require or encourage low-performing schools to replace teachers, assuming schools will recruit high-performing teachers who remain effective after transferring. However, teacher effectiveness may change after transferring, which could explain why some teacher replacement efforts do not improve student achievement. This paper contributes new information on the stability of teacher effectiveness by examining teachers who transfer into turnaround schools relative to teachers who transfer into low-performing but non-turnaround schools. I examine this issue using two turnaround models in Tennessee, one with documented positive effects and one producing no effects on student achievement. I find that teacher effectiveness increases after transferring into the model that produced positive effects and either decreases or stays the same after transferring into the model that did not improve student achievement. These findings suggest that heterogeneity in turnaround effects may be partly explained by changes in teacher effectiveness after they transfer into turnaround schools.
DOI: 10.5281/zenodo.7134729
title: Flow cytometry gating 2D: nD x (sangerP2.1)

description: Processed data set.
DOI: 10.5281/zenodo.7134728
title: Flow cytometry gating 2D: nD x (sangerP2.1)

description: Processed data set.
DOI: 10.34740/kaggle/dsv/4277930
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.20448920
title: Bagged Pretested Portfolio Selection

description: This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.
DOI: 10.6084/m9.figshare.20448920.v2
title: Bagged Pretested Portfolio Selection

description: This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.