cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cp29g
title: CCDC 2194995: Experimental Crystal Structure Determination

description: Related Article: Qingkun Wu, Jingxuan Hou, Qingshan Gu, Hui Gao, Meiqi Shi, Lu Zheng|2023|Org.Biomol.Chem.|21|1148|doi:10.1039/D2OB01712A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7415921
title: MD Simulation data for a pure POPE bilayer with AMOEBA force field + OpenMM

description: MD simulation data for the POPE bilayer with the AMOEBA-based force field developed by Li (https://doi.org/10.1080/00268976.2018.1436201). The simulation contains 72 POPE lipids and 2880 water molecules. The trajectory is 305,94 ns long (30594 frames with 10 ps saving frequency). <strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the openmm_combined.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. The correction to the timestamp was done via MDAnalysis.</strong> <strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong>
DOI: 10.5281/zenodo.7613095
title: Data set of Jodels corona channel from 2020-03-03 to 2021-02-13

description: This dataset contains all postings of the social media app Jodel that were published in the app's Corona channel in the German cities of Munich, Bonn, Rostock and Dresden within the period of March 3, 2020 to February 13, 2021. By examining the Corona channel, a relation of the postings to the topic of COVID-19 is given. For each posting, the following additional information is available in the data set: Post ID, publication time, channel, city, country, hashtags, downvotes, upvotes, replies, and shares. Jodel was released in Germany in 2014. The app allows users to publish anonymous postings that are displayed to users within a radius of about 10 km. The postings can be short messages, photos or videos. Users have the option to upvote or downvote postings published in the feed, comment on them, or share them outside the app.
DOI: 10.5281/zenodo.7613094
title: Data set of Jodels corona channel from 2020-03-03 to 2021-02-13

description: This dataset contains all postings of the social media app Jodel that were published in the app's Corona channel in the German cities of Munich, Bonn, Rostock and Dresden within the period of March 3, 2020 to February 13, 2021. By examining the Corona channel, a relation of the postings to the topic of COVID-19 is given. For each posting, the following additional information is available in the data set: Post ID, publication time, channel, city, country, hashtags, downvotes, upvotes, replies, and shares. Jodel was released in Germany in 2014. The app allows users to publish anonymous postings that are displayed to users within a radius of about 10 km. The postings can be short messages, photos or videos. Users have the option to upvote or downvote postings published in the feed, comment on them, or share them outside the app.
DOI: 10.5281/zenodo.7622879
title: osmoderma_eremita_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622878
title: osmoderma_eremita_spades_01.fasta.gz

description: .
DOI: 10.26025/1912/29667
title: WHOI_MetOcean_Nantucket

description: This data was collected by Kirincich and Cinquino as part of a Metocean monitoring campaign sponsored by the National Offshore Wind Research and Development Consortium (NOWRDC). The campaign was designed to observe key atmospheric parameters at a land based site in the proximity of the Massachusetts and Rhode Island Wind Energy Areas for one year. This is part of a longer term effort to collect atmospheric and oceanic data at an existing offshore platform in the vicinity, which can be found here: WHOI_MetOcean.

The campaign supported the purchase and installation of a LIDAR wind profiler at the Nantucket Test Site (NTS). This instrument was installed and operated by WHOI and validated by UL-AWS Truepower following a MetOcean Measurement Plan created by UL-AWST, WHOI, and NOWRDC. The LIDAR was installed at the NTS in November 2021 and operated continuously, as possible, through the end of 2023.

A LEOSPHERE WINDCUBE v2 was acquired from NRG Systems and deployed at the NTS after a previous deployment at WHOI's offshore Air Sea Interaction Tower (ASIT). The WIND-CUBE instrument is an industry approved LIDAR system for use in Wind Energy Resource Characterization studies. The choice of sensor was made in consultation with UL-AWS Truepower, the wind energy consultant advising MassCEC on its MetOcean Initiative. The lidar, serial number WLS7-436, was a leased model owned by NRG, the U.S. distributor for Leosphere. The instrument was installed at ground level in an open area and leveled following LEOSPHERE standards. A commissioning report for the lidar deployment are included as part of this repository.
DOI: 10.13026/edf9-9g37
title: MIMIC-IV-ED Demo

description: MIMIC-IV-ED is a publicly accessible database of over 400,000 emergency
department (ED) admissions to the Beth Israel Deaconess Medical Center between
2011 and 2019. Access to MIMIC-IV-ED requires registration on PhysioNet,
identity verification, completion of human participant training, and signing
of a data use agreement. Here, we have provided an openly available demo of
MIMIC-IV-ED containing a subset of 100 patients. Being a derivative dataset,
the MIMIC-IV-ED demo is deidentified according to the same standard as MIMIC-
IV-ED. The demo is intended to support workshops, educational material,
exploration of MIMIC-IV-ED, and other aims enabled by an openly available
dataset.
DOI: 10.13026/jzz5-vs76
title: MIMIC-IV-ED Demo

description: MIMIC-IV-ED is a publicly accessible database of over 400,000 emergency
department (ED) admissions to the Beth Israel Deaconess Medical Center between
2011 and 2019. Access to MIMIC-IV-ED requires registration on PhysioNet,
identity verification, completion of human participant training, and signing
of a data use agreement. Here, we have provided an openly available demo of
MIMIC-IV-ED containing a subset of 100 patients. Being a derivative dataset,
the MIMIC-IV-ED demo is deidentified according to the same standard as MIMIC-
IV-ED. The demo is intended to support workshops, educational material,
exploration of MIMIC-IV-ED, and other aims enabled by an openly available
dataset.
DOI: 10.13026/9cy4-f535
title: Chest X-ray Dataset with Lung Segmentation

description: Chest X-ray(CXR) images are prominent among medical images and are commonly
administered in emergency diagnosis and treatment corresponding to cardiac and
respiratory diseases. Though there are robust solutions available for medical
diagnosis, validation of artificial intelligence (AI) in radiology is still
questionable. Segmentation is pivotal in chest radiographs that aid in
improvising the existing AI-based medical diagnosis process. We provide the
CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large
dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a
popular CXR image dataset. The dataset contains segmentation results of
243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks.
Additionally, this dataset can be utilized for computer vision-related deep
learning tasks such as medical image classification, semantic segmentation and
medical report generation. Models using segmented images yield better results
since only the features related to the important areas of the image are
focused. Thus images of this dataset can be manipulated to any visual feature
extraction process associated with the original MIMIC-CXR dataset and enhance
the results of the published or novel investigations. Furthermore, masks
provided by this dataset can be used to train segmentation models when
combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80%
in dice similarity coefficient and 91.97% in IoU for lung segmentation using
CXLSeg.
DOI: 10.13026/pa5n-mc06
title: Chest X-ray Dataset with Lung Segmentation

description: Chest X-ray(CXR) images are prominent among medical images and are commonly
administered in emergency diagnosis and treatment corresponding to cardiac and
respiratory diseases. Though there are robust solutions available for medical
diagnosis, validation of artificial intelligence (AI) in radiology is still
questionable. Segmentation is pivotal in chest radiographs that aid in
improvising the existing AI-based medical diagnosis process. We provide the
CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large
dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a
popular CXR image dataset. The dataset contains segmentation results of
243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks.
Additionally, this dataset can be utilized for computer vision-related deep
learning tasks such as medical image classification, semantic segmentation and
medical report generation. Models using segmented images yield better results
since only the features related to the important areas of the image are
focused. Thus images of this dataset can be manipulated to any visual feature
extraction process associated with the original MIMIC-CXR dataset and enhance
the results of the published or novel investigations. Furthermore, masks
provided by this dataset can be used to train segmentation models when
combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80%
in dice similarity coefficient and 91.97% in IoU for lung segmentation using
CXLSeg.
DOI: 10.18112/openneuro.ds004478.v1.0.0
title: Neural activity induced by sensory stimulation can drive large-scale cerebrospinal fluid flow during wakefulness in humans
DOI: 10.13026/kdap-sg35
title: Annotation dataset of problematic opioid use and related contexts from MIMIC-III Critical Care Database discharge summaries

description: Opioid use disorder (OUD) is underdiagnosed in health system settings,
limiting research on OUD using electronic health records (EHRs). Medical
encounter notes can enrich structured EHR data with documented signs and
symptoms of OUD and social risks and behaviors. To capture this information at
scale, natural language processing tools must be developed and evaluated. We
conducted a pilot study that aimed to 1) develop and apply an annotation
schema to deeply characterize OUD and related clinical, behavioral, and
environmental factors; and 2) automate the annotation schema using machine
learning and deep learning-based approaches. De-identified patient data for
this study included hospital discharge summaries of patients with
_International Classification of Diseases_ (ICD-9) OUD diagnostic codes,
obtained from the MIMIC-III Critical Care Database. We developed an annotation
schema to characterize problematic opioid use, identify individuals with
potential OUD, and provide psychosocial context. The final annotation schema
contained 33 classes. Two annotators reviewed discharge summaries from a
random sample of 100 of these patients. The first corpus of 40 patients was
reviewed by both annotators. We achieved moderate inter-annotator agreement,
with F1-scores across all classes increasing from 48% to 66%. The second
corpus of 60 patients was reviewed by a single annotator. The shared database
contains the resulting 3,270 annotations with the note identifier, span offset
with accompanying text snippet, and class assignments and may be useful to
future development of natural language processing systems related to OUD.
DOI: 10.13026/tbb4-t319
title: Annotation dataset of problematic opioid use and related contexts from MIMIC-III Critical Care Database discharge summaries

description: Opioid use disorder (OUD) is underdiagnosed in health system settings,
limiting research on OUD using electronic health records (EHRs). Medical
encounter notes can enrich structured EHR data with documented signs and
symptoms of OUD and social risks and behaviors. To capture this information at
scale, natural language processing tools must be developed and evaluated. We
conducted a pilot study that aimed to 1) develop and apply an annotation
schema to deeply characterize OUD and related clinical, behavioral, and
environmental factors; and 2) automate the annotation schema using machine
learning and deep learning-based approaches. De-identified patient data for
this study included hospital discharge summaries of patients with
_International Classification of Diseases_ (ICD-9) OUD diagnostic codes,
obtained from the MIMIC-III Critical Care Database. We developed an annotation
schema to characterize problematic opioid use, identify individuals with
potential OUD, and provide psychosocial context. The final annotation schema
contained 33 classes. Two annotators reviewed discharge summaries from a
random sample of 100 of these patients. The first corpus of 40 patients was
reviewed by both annotators. We achieved moderate inter-annotator agreement,
with F1-scores across all classes increasing from 48% to 66%. The second
corpus of 60 patients was reviewed by a single annotator. The shared database
contains the resulting 3,270 annotations with the note identifier, span offset
with accompanying text snippet, and class assignments and may be useful to
future development of natural language processing systems related to OUD.
DOI: 10.25934/pr00007911.1
title: Available IPD datapackage for study 'A Multicenter, Prospective, Adaptive, Double-blind, Randomized, Placebo-controlled Study to Evaluate the Effect of Fluvoxamine, Ivermectin, Doxasozin and Interferon Lambda 1A in Mild COVID-19 and High Risk of Complications'

description: The COVID-19 pandemic has been characterized by high morbidity and mortality, especially in certain subgroups of patients. To date, no treatment has been shown to be effective in patients with early-onset disease and mild symptoms. Experimental studies have demonstrated a potential anti-inflammatory role of Fluvoxamine, Metformin and Ivermectin in SARS-CoV-2 infections and observational studies have suggested a reduced complications in patients with COVID-19 disease.
DOI: 10.6073/pasta/c9495d20407828932666c2311dd88b61
title: Blair et al. 2020: Machine learning identification of ground beetles (repackaging of occurrences published by the NEON Biorepository Data Portal)

description: Blair, J.; Weiser, M. D.; Kaspari, M.; Miller, M.; Siler, C.; Marshall, K. E. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10 (23): 13143-13153. https://doi.org/10.1002/ece3.6905 Additional NEON samples (not yet archived at the Biorepository) were used in this research: full list of occurrences used.
DOI: 10.5281/zenodo.7622905
title: pagurus_beringanus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622904
title: pagurus_beringanus_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.3761562.v2487
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.15783/c7m01m
title: CRAWDAD dataset copelabs/usense (v. 2016-03-17)

description: This data set comprises experiments carried out considering four Android devices, each named Usense 2, 3, 4, and 5, respectively. These devices were carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). All the data was collected each and every one minute.
description: Changelog: the initial version
DOI: 10.6073/pasta/a5ac63dc86badc1de1ca9e00c14476d5
title: Stachewicz et al. 2021: Trait correlation, phylogenetic signal in Carabidae morphology (repackaging of occurrences published by the NEON Biorepository Data Portal)

description: Stachewicz JD, Fountain-Jones NM, Koontz A, Woolf H, Pearse WD, Gallinat AS. 2021. Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology bioRxiv 02.12.431029; doi: https://doi.org/10.1101/2021.02.12.431029 Many NEON samples and specimens used in this work resulted from NEON prototype data and will not be archived in the Biorepository. See the appendices in the above linked article for a full list of NEON samples and specimens and their associated collection data. Additionally, see appendices of above linked article for specimen-level morphological trait measurements and genetic sequence data.
DOI: 10.15783/c7qs37
title: CRAWDAD dataset buffalo/phonelab-wifi (v. 2016-03-09)

description: Smartphones perform Wifi scans to adapt to the changing wireless environments causes by mobility. From network monitoring perspective, such scans provide a natural stream of network measurements from client's point of view. In order to see whether such measurements can provide new insights in monitoring large scale wireless networks, we collected the Wifi scan results data, together with other Wifi related logs, from the PhoneLab smartphone testbed over 5 months. All data are collected passively from the smartphones.
description: Changelog: the initial version
DOI: 10.6073/pasta/a457b80563a1553673f18f849fbdd29e
title: Staines & Staines 2021: The Geadephaga (Coleoptera: Carabidae and Rhysodidae) of SERC (repackaging of occurrences published by the NEON Biorepository Data Portal)

description: Linked records of Carabidae and Rhysodidae specimens from the SERC site, collected between 2015-2018, included in the following publication: Staines, C.L. & Staines, S. L. The Geadephaga (Coleoptera: Carabidae and Rhysodidae) of the Smithsonian Environmental Research Center, Maryland. 2021. Banisteria 55: 75-100. Original abstract: "An inventory of the Geadephaga (Coleoptera) at the Smithsonian Environmental Research Center, Anne Arundel County, Maryland is being conducted. Pitfall traps were placed and monitored from 2015 to 2018. From 2017 to 2020 directed collecting efforts were made to document the Geadephaga of the facility. A total of 111 Geadephaga species was collected: Carabidae - 110, Rhysodidae - 1." Research article available for download: https://virginianaturalhistorysociety.com/banisteria/pdf-files/ban55/Staines_SERC_Geadephaga.pdf
DOI: 10.15783/c74018
title: CRAWDAD dataset istanbul_technical/rssi (v. 2015-12-02)

description: Our measurement is performed in an urban
macrocell environment in the GSM 900 downlink
band based on real-life data. By a spectrum analyzer, 5 individual tracks with 10000 measurement data points are obtained.
Channel sounding is carried out at a GSM base station located at a height of 6 meters. 
At every track point measurements are taken in a
stationary fashion with 17ms sweep time on the
ground level. GPS information is also saved.
 the signal bandwidth
does not exceed the resolution bandwidth of the
spectrum analyzer, hence our measurements only
include path loss and shadow fading effects
Dataset is used in the article "Lognormal Mixture Shadowing".
description: Changelog: the initial version
DOI: 10.18430/m38bbu
title: X-ray diffraction data for the crystal structure of medical leech destabilase (high salt) (8BBU)
DOI: 10.18430/m38bbw
title: X-ray diffraction data for the crystal structure of medical leech destabilase (low salt) (8BBW)
DOI: 10.15783/c77s3w
title: CRAWDAD dataset iitkgp/apptraffic (v. 2015-11-26)

description: Traces from Android apps (primarily video) collected under different values of parameters, such as video length, connection strength and device mobility, for the purpose of mobile video app traffic pattern identification.
description: Changelog: the initial version
DOI: 10.15783/c7h883
title: CRAWDAD dataset ilesansfil/wifidog (v. 2015-11-06)

description: This data set contains user session traces which were collected from a large number of free Wi-Fi hotspots in Montreal, Quebec, Canada for six years.
description: Changelog: the initial version
DOI: 10.15783/c7n01x
title: CRAWDAD dataset init/factory (v. 2015-09-18)

description: Measurement of the channel gain for multiple distances within a factory environment
description: Changelog: the initial version
DOI: 10.18430/m38fuy
title: X-ray diffraction data for the glucose-6-phosphate 1-dehydrogenase (G6PDH) from Crithidia fasciculata (citrate bound) (8FUY)
DOI: 10.5281/zenodo.7622922
title: heteromormyrus_pauciradiatus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7622921
title: heteromormyrus_pauciradiatus_spades_01.fasta.gz

description: .
DOI: 10.15783/c7wg6t
title: CRAWDAD dataset vt/maniac (v. 2011-07-21)

description: The dataset comprises routing and topology traces collected during the Mobile 
Ad hoc Networks Interoperability And Cooperation (MANIAC) Challenges, held on 
November 25-26th 2007 in conjunction with IEEE Globecom 2007 and on March 8, 
2009 in conjunction with IEEE PerCom 2009.
description: Changelog: Traceset vt/maniac/2009 has been added.
DOI: 10.6084/m9.figshare.22056707
title: Formalise2023

description: Formalise2023
DOI: 10.6073/pasta/1a282901cc871485c7ead0b2a54ff859
title: Decomissioned Site: Arthur Brook's (D01 ARTH) Legacy and Prototype Aquatic invertebrates (repackaging of occurrences published by the NEON Biorepository Data Portal)

description: This collection contains legacy and prototype NEON aquatic invertebrates from the decommissioned Arthur Brook's site in Worcester County, Massachusets. These samples and specimens were collected using NEON protocol NEON.DP1.20120 and are summarized in NEON Prototype dataset e7d152a1-1181-4c7e-ac75-ae707ffc7299. This data is available here.
DOI: 10.5281/zenodo.7592885
title: Automated Program Repair in the Era of Large Pre-trained Language Models

description: Code used for the paper along with the generated outputs
DOI: 10.5281/zenodo.7592886
title: Automated Program Repair in the Era of Large Pre-trained Language Models

description: Code used for the paper along with the generated outputs
DOI: 10.5281/zenodo.7622931
title: Automated Program Repair in the Era of Large Pre-trained Language Models

description: Code used for the paper along with the generated outputs