cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5121167
title: Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making - raw data

description: Raw EEG and behavioral data of the study "Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making"
DOI: 10.5281/zenodo.5121166
title: Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making - raw data

description: Raw EEG and behavioral data of the study "Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making"
DOI: 10.34810/data72/1034
title: 03AC_1.MR.0004.0105.2013.03.24.12.28.52.968750.171713524.IMA
DOI: 10.5281/zenodo.4774960
title: MATS - Multi-Annotator Tagged Soundscapes

description: This is a dataset containing <strong>audio tags</strong> for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool, with multiple annotators providing labels for each file. The dataset contains annotations for 3930 files, annotated with the following tags: announcement jingle announcement speech adults talking birds singing children voices dog barking footsteps music siren traffic noise The annotation procedure and processing is presented in the paper: Irene Martin-Morato, Annamaria Mesaros. <em>What is the ground truth? Reliability of multi-annotator data for audio tagging, </em>29th European Signal Processing Conference, EUSIPCO 2021 The dataset contains the following: <strong>raw annotations</strong> provided by 133 annotators, multiple opinions per audio file <strong> </strong>MATS_labels_full_annotations.yaml<em> </em> content formatted as: - filename: file1.wav <br> annotations:<br> - annotator_id: ann_1<br> tags:<br> - tag1<br> - tag2<br> - annotator_id: ann_3<br> tags:<br> - tag1<br> - filename: file3.wav <br> ... <strong>processed annotations</strong> using different methods, as presented in the accompanying paper MATS_labels_majority_vote.csv<br> MATS_labels_union.csv<br> MATS_labels_mace100.csv <br> MATS_labels_mace100_competence60 content formatted as: filename [tab] tag1,tag2,tag3 <br> The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.
description: {"references": ["Irene Martin-Morato, Annamaria Mesaros. What is the ground truth? Reliability of multi-annotator data for audio tagging, 29th European Signal Processing Conference, EUSIPCO 2021, https://arxiv.org/abs/2104.04214"]}
DOI: 10.5281/zenodo.4774959
title: MATS - Multi-Annotator Tagged Soundscapes

description: This is a dataset containing <strong>audio tags</strong> for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool, with multiple annotators providing labels for each file. The dataset contains annotations for 3930 files, annotated with the following tags: announcement jingle announcement speech adults talking birds singing children voices dog barking footsteps music siren traffic noise The annotation procedure and processing is presented in the paper: Irene Martin-Morato, Annamaria Mesaros. <em>What is the ground truth? Reliability of multi-annotator data for audio tagging, </em>29th European Signal Processing Conference, EUSIPCO 2021 The dataset contains the following: <strong>raw annotations</strong> provided by 133 annotators, multiple opinions per audio file <strong> </strong>MATS_labels_full_annotations.yaml<em> </em> content formatted as: - filename: file1.wav <br> annotations:<br> - annotator_id: ann_1<br> tags:<br> - tag1<br> - tag2<br> - annotator_id: ann_3<br> tags:<br> - tag1<br> - filename: file3.wav <br> ... <strong>processed annotations</strong> using different methods, as presented in the accompanying paper MATS_labels_majority_vote.csv<br> MATS_labels_union.csv<br> MATS_labels_mace100.csv <br> MATS_labels_mace100_competence60 content formatted as: filename [tab] tag1,tag2,tag3 <br> The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.
description: {"references": ["Irene Martin-Morato, Annamaria Mesaros. What is the ground truth? Reliability of multi-annotator data for audio tagging, 29th European Signal Processing Conference, EUSIPCO 2021, https://arxiv.org/abs/2104.04214"]}
DOI: 10.34810/data72/1035
title: 03AC_1.MR.0004.0098.2013.03.24.12.28.52.968750.171713398.IMA
DOI: 10.34810/data72/1036
title: 03CO_1.MR.0004.0174.2016.02.04.15.48.15.906250.67241987.IMA
DOI: 10.5281/zenodo.4762163
title: Supplementary material: Dynamical mean-field theory of the Anderson–Hubbard model with local and non-local disorder in tensor formulation

description: <strong>Supplementary material</strong><br> ====================== This is the supplementary material to the article<br> ``Dynamical mean-field theory of the Anderson–Hubbard model with local and<br> non-local disorder in tensor formulation``. It contains the DMFT data, the scripts to produces the figures, and some numerical<br> tests for some of the given equations <br> <strong>Scripts</strong><br> ------- `Python3` (at least 3.6) is required to run the scripts,<br> the necessary dependencies are specified by `requirements.txt`:: $ pip install -r requirements.txt In case there are dependency problems, pipenv can be used to reproduce the<br> exact environment used to run the scripts:: $ pip install pipenv<br> $ pipenv install<br> $ pipenv run shell <br> <strong>Data</strong><br> ---- The directory `DMFT_data` contains the final iteration of the DMFT calculations<br> shown in the article. The directories are named after the parameters,<br> where `U` is the Hubbard interaction, `V` the on-site energy, and<br> `tab` the hopping parameter between components `A` and `B`.<br> If parameters differ for the components they are given as `Ua`, `Ub` … Each directory contains the subdirectories `lattice_output` with the (local)<br> lattice quantifies and `impurity_ouput` with the results of the effective<br> impurity models (SIAMs). The data is provided as `HDF5` files, it can be conveniently processed and<br> visualized using Python's xarray module.<br> A convenient `open_dataset` method is provided in `plotting_scripts/dataio.py`. <br> <strong>Figures</strong><br> ------- The directory `plotting_scripts` contains the Python scripts to generate the<br> figures. They are prefixed with `figN_` according to the figure numbers. <br> <strong>Tests</strong><br> ----- The directory `test_scripts` contains some numerical test for formulas given in<br> the article. The files prefixed with `test_` can be executed by running pytest. The scripts prefixed with `frequency_expansion_` provide only a visual check<br> of the high-frequency expansion, they are directly run with `python`. You can<br> verify by eye that the BEB effective medium converges against the high-frequency<br> moments given in the article.
DOI: 10.5281/zenodo.4762162
title: Supplementary material: Dynamical mean-field theory of the Anderson–Hubbard model with local and non-local disorder in tensor formulation

description: <strong>Supplementary material</strong><br> ====================== This is the supplementary material to the article<br> ``Dynamical mean-field theory of the Anderson–Hubbard model with local and<br> non-local disorder in tensor formulation``. It contains the DMFT data, the scripts to produces the figures, and some numerical<br> tests for some of the given equations <br> <strong>Scripts</strong><br> ------- `Python3` (at least 3.6) is required to run the scripts,<br> the necessary dependencies are specified by `requirements.txt`:: $ pip install -r requirements.txt In case there are dependency problems, pipenv can be used to reproduce the<br> exact environment used to run the scripts:: $ pip install pipenv<br> $ pipenv install<br> $ pipenv run shell <br> <strong>Data</strong><br> ---- The directory `DMFT_data` contains the final iteration of the DMFT calculations<br> shown in the article. The directories are named after the parameters,<br> where `U` is the Hubbard interaction, `V` the on-site energy, and<br> `tab` the hopping parameter between components `A` and `B`.<br> If parameters differ for the components they are given as `Ua`, `Ub` … Each directory contains the subdirectories `lattice_output` with the (local)<br> lattice quantifies and `impurity_ouput` with the results of the effective<br> impurity models (SIAMs). The data is provided as `HDF5` files, it can be conveniently processed and<br> visualized using Python's xarray module.<br> A convenient `open_dataset` method is provided in `plotting_scripts/dataio.py`. <br> <strong>Figures</strong><br> ------- The directory `plotting_scripts` contains the Python scripts to generate the<br> figures. They are prefixed with `figN_` according to the figure numbers. <br> <strong>Tests</strong><br> ----- The directory `test_scripts` contains some numerical test for formulas given in<br> the article. The files prefixed with `test_` can be executed by running pytest. The scripts prefixed with `frequency_expansion_` provide only a visual check<br> of the high-frequency expansion, they are directly run with `python`. You can<br> verify by eye that the BEB effective medium converges against the high-frequency<br> moments given in the article.
DOI: 10.6084/m9.figshare.15001371.v1
title: CATAMARAN_v1.json

description: Cross-lingual summarization, which produces a summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of cross-lingual summarization datasets. CATAMARAN is the first high-quality cross-lingual long text abstractive summarization dataset. It is crawled from the New York Times Chinese and English websites and contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average length of articles is 1133.65 for English articles and 2035.33 for Chinese articles, and the average length of the summaries is 26.59 and 70.05, respectively.
DOI: 10.5281/zenodo.5121182
title: Data and software associated with the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences"

description: 1. Data files and run logs produced for the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences". 2. Software script versions used in the above.
description: For software, see also https://github.com/helmutsimon/bayescoalescentest
DOI: 10.5281/zenodo.5121181
title: Data and software associated with the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences"

description: 1. Data files and run logs produced for the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences". 2. Software script versions used in the above.
description: For software, see also https://github.com/helmutsimon/bayescoalescentest
DOI: 10.34810/data72/1037
title: 03AC_1.MR.0004.0088.2013.03.24.12.28.52.968750.171713218.IMA
DOI: 10.34810/data72/1038
title: 03CO_1.MR.0004.0240.2016.02.04.15.48.15.906250.67245592.IMA
DOI: 10.34810/data72/1039
title: 03AC_1.MR.0004.0175.2013.03.24.12.28.52.968750.171714784.IMA
DOI: 10.6084/m9.figshare.15001371
title: CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset

description: Cross-lingual summarization, which produces a summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of cross-lingual summarization datasets. CATAMARAN is the first high-quality cross-lingual long text abstractive summarization dataset. It is crawled from the New York Times Chinese and English websites and contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average length of articles is 1133.65 for English articles and 2035.33 for Chinese articles, and the average length of the summaries is 26.59 and 70.05, respectively.
DOI: 10.6084/m9.figshare.15001371.v3
title: CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset

description: Cross-lingual summarization, which produces a summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of cross-lingual summarization datasets. CATAMARAN is the first high-quality cross-lingual long text abstractive summarization dataset. It is crawled from the New York Times Chinese and English websites and contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average length of articles is 1133.65 for English articles and 2035.33 for Chinese articles, and the average length of the summaries is 26.59 and 70.05, respectively.
DOI: 10.6084/m9.figshare.15001371.v2
title: CATAMARAN: A Cross-lingual Long Text Abstractive1Summarization Dataset

description: Cross-lingual summarization, which produces a summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of cross-lingual summarization datasets. CATAMARAN is the first high-quality cross-lingual long text abstractive summarization dataset. It is crawled from the New York Times Chinese and English websites and contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average length of articles is 1133.65 for English articles and 2035.33 for Chinese articles, and the average length of the summaries is 26.59 and 70.05, respectively.
DOI: 10.34810/data72/1040
title: 03AC_1.MR.0004.0235.2013.03.24.12.28.52.968750.171715871.IMA
DOI: 10.5281/zenodo.4729908
title: HHD_gender

description: The HHD_gender dataset contains 819 handwritten forms written by volunteers of different educational backgrounds and ages (as young as 11 years old and as old as late 60s), both native and non-native Hebrew speakers. There are 50 variations of the forms; each form contains a text paragraph with 62 words on average. For the experiments, the HHD gender dataset was randomly subdivided into training (80%), validation (10%), and test (10%) sets. --------------------------------------------------------------------------------------------------------------- This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to the following papers: [1] I. Rabaev, B. Kurar Barakat, A. Churkin and J. El-Sana. The HHD Dataset. The 17th International Conference on Frontiers in Handwriting Recognition, pp. 228-233, 2020, DOI: 10.1109/ICFHR2020.2020.00050 [2] I. Rabaev, M. Litvak, S. Asulin and O.H. Tabibi. Automatic Gender Classification from Handwritten Images: a Case Study, the 19th International Conference on Computer Analysis of Images and Patterns, 2021.
DOI: 10.5281/zenodo.4729907
title: HHD_gender

description: The HHD_gender dataset contains 819 handwritten forms written by volunteers of different educational backgrounds and ages (as young as 11 years old and as old as late 60s), both native and non-native Hebrew speakers. There are 50 variations of the forms; each form contains a text paragraph with 62 words on average. For the experiments, the HHD gender dataset was randomly subdivided into training (80%), validation (10%), and test (10%) sets. --------------------------------------------------------------------------------------------------------------- This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to the following papers: [1] I. Rabaev, B. Kurar Barakat, A. Churkin and J. El-Sana. The HHD Dataset. The 17th International Conference on Frontiers in Handwriting Recognition, pp. 228-233, 2020, DOI: 10.1109/ICFHR2020.2020.00050 [2] I. Rabaev, M. Litvak, S. Asulin and O.H. Tabibi. Automatic Gender Classification from Handwritten Images: a Case Study, the 19th International Conference on Computer Analysis of Images and Patterns, 2021.
DOI: 10.15468/24a5qq
title: Vaccinium exiguum (Ericaceae, Vaccinieae), a new species from the ultramafic summit of Mt. Victoria, Palawan Island, Philippines

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tamayo, Maverick N., Bustamante, Rene Alfred Anton, Fritsch, Peter W. (2021): Vaccinium exiguum (Ericaceae, Vaccinieae), a new species from the ultramafic summit of Mt. Victoria, Palawan Island, Philippines. PhytoKeys 179: 145-154, DOI: http://dx.doi.org/10.3897/phytokeys.179.68323, URL: http://dx.doi.org/10.3897/phytokeys.179.68323
DOI: 10.34810/data72/1041
title: 03CO_1.MR.0004.0182.2016.02.04.15.48.15.906250.67242300.IMA
DOI: 10.5281/zenodo.5084655
title: Frontmatter: Mining Android User Interfaces at Scale

description: Frontmatter dataset contains user interface models of about 160,000 Android apps.
DOI: 10.5281/zenodo.5084654
title: Frontmatter: Mining Android User Interfaces at Scale

description: Frontmatter dataset contains user interface models of about 160,000 Android apps.
DOI: 10.34810/data72/1042
title: 03AC_1.MR.0004.0097.2013.03.24.12.28.52.968750.171713380.IMA
DOI: 10.5281/zenodo.5118948
title: AMFinder training dataset

description: Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. The software AMFinder allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. <strong>This dataset contains ink-stained root images used for AMFinder training.</strong>
DOI: 10.5281/zenodo.5118947
title: AMFinder training dataset

description: Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. The software AMFinder allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. <strong>This dataset contains ink-stained root images used for AMFinder training.</strong>
DOI: 10.6084/m9.figshare.15034746
title: Supplementary Material for: Ferric Citrate Dosing in Iron Deficiency Anemia in Nondialysis-Dependent Chronic Kidney Disease

description: <b><i>Introduction:</i></b> Ferric citrate (FC) is indicated as an oral iron replacement for iron deficiency anemia in adult patients with chronic kidney disease (CKD) not on dialysis. The recommended starting dose is one 1-g tablet three times daily (TID). This study investigated long-term efficacy and safety of different FC dosing regimens for treating anemia in nondialysis-dependent CKD (NDD-CKD). <b><i>Methods:</i></b> In this phase 4, randomized, open-label, multicenter study, patients with anemia with NDD-CKD (estimated glomerular filtration rate, ≥20 mL/min and &lt;60 mL/min) were randomized 1:1 to one FC tablet (1-g equivalent to 210 mg ferric iron) TID (3 g/day) or 2 tablets twice daily (BID; 4 g/day). At week 12, dosage was increased to 2 tablets TID (6 g/day) or 3 tablets BID (6 g/day) in patients whose hemoglobin (Hb) levels increased &lt;0.5 g/dL or were &lt;10 g/dL. Primary endpoint was mean change in Hb from baseline to week 24. <b><i>Results:</i></b> Of 484 patients screened, 206 were randomized and 205 received FC. Mean (standard deviation) changes from baseline in Hb at week 24 were 0.77 (0.84) g/dL with FC TID 3 g/day and 0.70 (0.98) g/dL with FC BID 4 g/day. <b><i>Discussion/Conclusions:</i></b> FC administered BID and TID for 48 weeks was safe and effective for treating anemia in this population, supporting potentially increased dosing flexibility.
DOI: 10.34810/data72/1043
title: 03CO_1.MR.0004.0001.2016.02.04.15.48.15.906250.67228047.IMA
DOI: 10.6084/m9.figshare.15034752
title: EPC_data

description: This is a test dataset.
DOI: 10.6084/m9.figshare.15034752.v1
title: EPC_data

description: This is a test dataset.
DOI: 10.34810/data72/1044
title: 03AC_1.MR.0004.0206.2013.03.24.12.28.52.968750.171715349.IMA
DOI: 10.6084/m9.figshare.15034770
title: Supplementary Material for: Metabolic Disorders/Obesity Is a Primary Risk Factor in Hidradenitis Suppurativa: An Immunohistochemical Real-World Approach

description: <b><i>Background:</i></b> Hidradenitis suppurativa (HS) is an inflammatory, potentially scarring disease of the hair follicle, affecting the apocrine gland-bearing skin areas. The major comorbid disorders associated with the occurrence or the aggravation of the disease are obesity and smoking. Numerous efforts to dissociate these factors led to controversial results. <b><i>Objectives:</i></b> To assess the importance of metabolic disorders/obesity, smoking/environmental toxins, and inflammation in HS by utilizing the differential expression of major relevant protein markers in lesional skin of obese/smoking versus non-obese/non-smoking HS patients. <b><i>Methods:</i></b> Lesional skin specimens deriving from two groups of HS patients (BMI &gt;30 and smokers, <i>n</i> = 12 vs. BMI &lt;30 and non-smokers, <i>n</i> = 10) were stained with antibodies raised against irisin, PPARγ, and IGF-1R, which correlate with metabolic disorders/obesity, EGFR and AhR, associated with smoking, and IL-17, IL-17R, and S100A8, as markers of inflammation. <b><i>Results:</i></b> Metabolic disorders/obesity-related markers exhibited marked differential expression between the two groups, while smoking-associated markers a limited one. IL-17R expression was stronger in obese/smokers, and S100A8 staining exhibited intense strong immunoreactivity in both groups without significant difference. <b><i>Conclusions:</i></b> The notion that obesity plays a role in HS development appears to be supported by the prominent regulation of the associated lesional biomarkers. Tobacco smoking might contribute less to HS than previously suspected.