cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc287yzg
title: CCDC 2093026: Experimental Crystal Structure Determination

description: Related Article: Huatian Shi, Hung Kay Lee, Yi Pan, Kai-Chung Lau, Shek-Man Yiu, William W. Y. Lam, Wai-Lun Man, Tai-Chu Lau|2021|J.Am.Chem.Soc.|143|15863|doi:10.1021/jacs.1c08072
DOI: 10.5517/ccdc.csd.cc287z0j
title: CCDC 2093027: Experimental Crystal Structure Determination

description: Related Article: Huatian Shi, Hung Kay Lee, Yi Pan, Kai-Chung Lau, Shek-Man Yiu, William W. Y. Lam, Wai-Lun Man, Tai-Chu Lau|2021|J.Am.Chem.Soc.|143|15863|doi:10.1021/jacs.1c08072
DOI: 10.5517/ccdc.csd.cc288ss4
title: CCDC 2093826: Experimental Crystal Structure Determination

description: Related Article: Inzamam Abbasi, Humaira Nadeem, Adil Saeed, Hafiz Aamir Ali Kharl, Muhammad Nawaz Tahir, Muhammad Moazzam Naseer|2021|Bioorg.Chem.|116|105385|doi:10.1016/j.bioorg.2021.105385
DOI: 10.5517/ccdc.csd.cc28bj38
title: CCDC 2095479: Experimental Crystal Structure Determination

description: Related Article: Jiaming Li, Jian Li, Xiaoliang Ji, Runfa He, Yang Liu, Zebin Chen, Yubing Huang, Qiang Liu, Yibiao Li|2021|Org.Lett.|23|7412|doi:10.1021/acs.orglett.1c02600
DOI: 10.5517/ccdc.csd.cc28ljkz
title: CCDC 2103181: Experimental Crystal Structure Determination

description: Related Article: Lihong Wang, Kaibo Zheng, Wei Yu, Jiaying Yan, Nuonuo Zhang|2021|Inorg.Chem.Commun.|133|108932|doi:10.1016/j.inoche.2021.108932
DOI: 10.5517/ccdc.csd.cc28n267
title: CCDC 2104658: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Sierra R. Ciccone, William N. G. Moore, Joseph W. Ziller, William J. Evans|2021|Polyhedron|210|115493|doi:10.1016/j.poly.2021.115493
DOI: 10.5517/ccdc.csd.cc28nf2g
title: CCDC 2104995: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Sierra R. Ciccone, William N. G. Moore, Joseph W. Ziller, William J. Evans|2021|Polyhedron|210|115493|doi:10.1016/j.poly.2021.115493
DOI: 10.5517/ccdc.csd.cc28nf3h
title: CCDC 2104996: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Sierra R. Ciccone, William N. G. Moore, Joseph W. Ziller, William J. Evans|2021|Polyhedron|210|115493|doi:10.1016/j.poly.2021.115493
DOI: 10.5517/ccdc.csd.cc28nf4j
title: CCDC 2104997: Experimental Crystal Structure Determination

description: Related Article: Daniel N. Huh, Sierra R. Ciccone, William N. G. Moore, Joseph W. Ziller, William J. Evans|2021|Polyhedron|210|115493|doi:10.1016/j.poly.2021.115493
DOI: 10.6084/m9.figshare.16780138
title: Dataset with all the computed metrics per sub-sequence

description: Intermediate dataset, comprising all subsequences contextual variables and extracted metrics.<br>Dataset created after running the first part of the script also uploaded in this FigShare depository.<br>
DOI: 10.6084/m9.figshare.16780138.v1
title: Dataset with all the computed metrics per sub-sequence

description: Intermediate dataset, comprising all subsequences contextual variables and extracted metrics.<br>Dataset created after running the first part of the script also uploaded in this FigShare depository.<br>
DOI: 10.6084/m9.figshare.16780132
title: Dataset with additional information on the audio recordings from predator presentations

description: Dataset used to run the script (also in the FigShare depository), extracted from the additionnal information recorded from the 58 audio recordings of the vocal reaction of titi monkeys when presented to three stuffed predators (tayra, oncilla and caracara). <br><br>Dataset creation: Additional information was collected by Berthet et al. and Cäsar et al. and entered into different excel files. We've lately included this information in the new dataset (to have all informations in the same format), which is the one we will be using in our script.<br>
DOI: 10.6084/m9.figshare.16780123.v1
title: Dataset extracted from audio recordings from predator presentations (first 50 calls)

description: Dataset used to run the code (also in the FigShare depository), extracted from 58 audio recordings of the vocal reaction of titi monkeys when presented to three stuffed predators (tayra, oncilla and caracara).<br>Coding methodology: We've used the vocal repertoire described by Cäsar et al. (2012), which includes two alarm calls: A- and B-calls. Audios were listened with Praat, and the first 50 calls of each sequence were labelled as A-calls, B-calls or "other" (undetermined call or other call type). The beginning and ending time of each call was also labellised.<br>You can find the recordings collected by Berthet et al. (2019) in the following link : https://figshare.com/articles/dataset/Audio_recordings_of_predator_presentations/7707119. Recordings collected by Cäsar must be requested directly to the author.
DOI: 10.6084/m9.figshare.16780123
title: Dataset extracted from audio recordings from predator presentations (first 50 calls)

description: Dataset used to run the code (also in the FigShare depository), extracted from 58 audio recordings of the vocal reaction of titi monkeys when presented to three stuffed predators (tayra, oncilla and caracara).<br>Coding methodology: We've used the vocal repertoire described by Cäsar et al. (2012), which includes two alarm calls: A- and B-calls. Audios were listened with Praat, and the first 50 calls of each sequence were labelled as A-calls, B-calls or "other" (undetermined call or other call type). The beginning and ending time of each call was also labellised.<br>You can find the recordings collected by Berthet et al. (2019) in the following link : https://figshare.com/articles/dataset/Audio_recordings_of_predator_presentations/7707119. Recordings collected by Cäsar must be requested directly to the author.
DOI: 10.6084/m9.figshare.16780117.v1
title: DataVarExplained.txt

description: This is the final dataset, computed by the code included in this folder (ungency_principle_and_titi_monkey-s_alarm_system).<br>
DOI: 10.14272/duwzslvpqikyhu-uhfffaoysa-n/chmo0000470.2
title: mass spectrometry (MS) ((3-phenylquinoxalin-2-yl)hydrazine)
title: InChI=1S/C14H12N4/c15-18-14-13(10-6-2-1-3-7-10)16-11-8-4-5-9-12(11)17-14/h1-9H,15H2,(H,17,18)

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 140 °C): 236 (100) [M]+, 235 (39), 221 (29), 220 (48), 219 (23), 218 (18), 207 (21), 206 (15), 205 (11), 104 (12), 103 (12), 102 (12), 91 (16), 90 (29), 77 (34), 76 (17), 69 (13), 58 (12), 57 (10), 51 (12). HRMS–EI (m/z): [M]+ calcd for C14H12N4, 236.1062; found, 236.1063.
DOI: 10.34740/kaggle/dsv/2692620
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.16780198
title: Supplementary Table S2-S4.xlsx

description: the total number of proteins identified and all detected differentially expressed proteins
DOI: 10.6084/m9.figshare.16780198.v1
title: Supplementary Table S2-S4.xlsx

description: the total number of proteins identified and all detected differentially expressed proteins
DOI: 10.34740/kaggle/dsv/2692626
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.5559504
title: Ivindomyrus marchei SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5559503
title: Ivindomyrus marchei SPAdes preassembly

description: .
DOI: 10.25345/c5c278
title: MassIVE MSV000088208 - The membrane proteome of spores and vegetative cells of the food-borne pathogen Bacillus cereus.
title: Membrane proteins are fascinating since they play an important role in diverse cellular functions and constitute many drug targets. Membrane proteins are challenging to analyze. The spore, the most resistant form of life known, harbors a compressed inner membrane. This membrane acts not only as a barrier for undesired molecules but also as a scaffold for proteins involved in signal transduction and the transport of metabolites during spore germination and subsequent vegetative growth. In this study, we adapted a membrane enrichment method to study the membrane proteome of spores and cells of the food-borne pathogen Bacillus cereus using quantitative proteomics. Using bioinformatics filtering we identify and quantify 498 vegetative cell membrane proteins and 244 spore inner membrane proteins. Comparison of vegetative and spore membrane proteins showed there were 54 spore membrane specific and 308 cell membrane specific proteins. Functional characterization of these proteins showed that the cell membrane proteome has a far larger number of transporters, receptors and proteins related to cell division and motility. This was also reflected in the much higher expression level of many of these proteins in the cellular membrane for those proteins that were in common with the spore inner membrane. The spore inner membrane had specific expression of several germinant receptor and spore specific proteins, but also seemed to show a preference towards the use of simple carbohydrates like glucose and fructose owing to only expressing transporters for these. These results show the differences in membrane proteome composition and show us the specific proteins necessary in the inner membrane of a dormant spore of this toxigenic spore forming bacterium to survive adverse conditions.
DOI: 10.34740/kaggle/dsv/2692656
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.5559543
title: Erythroxylum densum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5559542
title: Erythroxylum densum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.1319659
title: UC2017 Static and Dynamic Hand Gestures

description: We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction. We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem. The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand. We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time. The files are in the h5df format. The dimensions are (sample, time, variables).
DOI: 10.5281/zenodo.1319658
title: UC2017 Static and Dynamic Hand Gestures

description: We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction. We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem. The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand. We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time. The files are in the h5df format. The dimensions are (sample, time, variables).
DOI: 10.5281/zenodo.5559547
title: Gallinago delicata SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5559546
title: Gallinago delicata SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5559566
title: Labidopullus appendiculatus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5559565
title: Labidopullus appendiculatus SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.16780255
title: Raw Data

description: raw data of the study
DOI: 10.6084/m9.figshare.16780255.v1
title: Raw Data

description: raw data of the study
DOI: 10.5281/zenodo.5559595
title: Dataset for master's thesis reproduction package

description: This is the data needed to reproduce the results shown in my master's thesis <em>"Measuring the Energy Consumption ofSoftware written in C on x86-64 Processors"</em>.
DOI: 10.5281/zenodo.5550107
title: Dataset for master's thesis reproduction package

description: This is the data needed to reproduce the results shown in my master's thesis <em>"Measuring the Energy Consumption ofSoftware written in C on x86-64 Processors"</em>.
DOI: 10.5281/zenodo.5559607
title: Ara chloropterus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5559606
title: Ara chloropterus decontaminated FSCR

description: .
DOI: 10.5519/qd.51onxj39
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (803 records)
DOI: 10.5281/zenodo.5559617
title: Ara glaucogularis decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5559616
title: Ara glaucogularis decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.16681900.v2
title: MSA (SARS-CoV-2)

description: 30,229 SARS-CoV-2 sequences within the timeframe from 31 December 2019 to 22 March 2021 were retrieved from the GISAID database (https://www.gisaid.org/). Multiple sequence alignment (MSA) of the sequences was performed using MAFFT with the parameters left as default.
DOI: 10.5281/zenodo.5559620
title: Raw Data

description: It is a file for raw data.
DOI: 10.5281/zenodo.5559619
title: Raw Data

description: It is a file for raw data.
DOI: 10.6084/m9.figshare.16780333
title: SokNes Officials

description: This is a list of officials from Roman Egypt and the village of Soknopaiou Nesos in the Fayum. It is supporting data for a paper on Village officials and literacy in the roman Empire.
DOI: 10.6084/m9.figshare.16780333.v1
title: SokNes Officials

description: This is a list of officials from Roman Egypt and the village of Soknopaiou Nesos in the Fayum. It is supporting data for a paper on Village officials and literacy in the roman Empire.
DOI: 10.6084/m9.figshare.16780342
title: SokNes Texts

description: This is a list if documentary texts from first Century CE Soknopaiou Nesos in the Fayum, Egypt
DOI: 10.6084/m9.figshare.16780342.v1
title: SokNes Texts

description: This is a list if documentary texts from first Century CE Soknopaiou Nesos in the Fayum, Egypt
DOI: 10.5281/zenodo.5559641
title: Dataset of Bnet values of 21 radiation damage series analysed in "Quantifying and comparing radiation damage in the Protein Data Bank"

description: Dataset of Bnet values, plus associated metadata (resolution, Rwork, Rfree, temperature, etc.), for 21 radiation damage series deposited in the PDB that are analysed in the publication "Quantifying and comparing radiation damage in the Protein Data Bank"
DOI: 10.5281/zenodo.5559646
title: Dataset of Bnet values of 93,978 PDB-REDO structures analysed in "Quantifying and comparing radiation damage in the Protein Data Bank"

description: Dataset of Bnet values, plus associated metadata (resolution, Rwork, Rfree, temperature, etc.), for 93,978 PDB-REDO structures analysed in the publication "Quantifying and comparing radiation damage in the Protein Data Bank"
DOI: 10.6084/m9.figshare.16780354
title: SokNes writers

description: This is a list of named writers in Greek from Roman Soknopaiou Nesos in the first CE, in Egypt, Fayum Depression.
DOI: 10.6084/m9.figshare.16780354.v1
title: SokNes writers

description: This is a list of named writers in Greek from Roman Soknopaiou Nesos in the first CE, in Egypt, Fayum Depression.
DOI: 10.5281/zenodo.5513472
title: ESD Study Results

description: Data and R Scripts necessary to reproduce results of ESD study at NUIG. The study aimed to investigate whether Systems Thinking (ST) and/or System Dynamics simulation (Sim) increased the effectiveness of Sustainability Education. In the study, participants were randomly allocated to one of four groups, and interacted with an online learning tool which contained embedded quizzes. Their performance in these quizzes formed the basis for comparison between groups. An open access of the learning tool is available here: https://exchange.iseesystems.com/public/carolineb/sustainability-learning-tool/. The <em>R Scripts</em> folder contains 4 R scripts used for data analysis. Run the script function_qualitative.R first. The <em>data </em>folder contains the anonymised research data. Create a folder called <em>data </em>beneath the folder containing your R scripts. The <em>Surveys</em> folder is for reference only. It contains the surveys and quizzes used to collect the data, together with quiz marking schemes, code books and quiz answers. In the data, group members are identified by a number from 0 to 3: group 0 means control group, group 1 means ST group, group 2 means Sim group and group 3 means ST + Sim group. For more details see README.md and the readme.txt files in each folder.
description: Additional funding from the Higher Education Authority (HEA), Ireland.
DOI: 10.48531/jbru.calmip/fem6n9/yroqa9
title: Waste-to-nutrition-Supplementing-Information.pdf

description: This file displays a selection of examples from the database, along with a unit operation illustrating figure for each waste-to-nutrition pathway category
DOI: 10.48531/jbru.calmip/fem6n9/vw5zku
title: Waste-to-nutrition-Database_To-read.pdf

description: Description and additional information on the Waste-to-nutrition database
DOI: 10.48531/jbru.calmip/fem6n9/hjoria
title: Waste-to-nutrition-Database.tab

description: The Excel file details the methodology and information gathered to complete the literature review
DOI: 10.48531/jbru.calmip/fem6n9/hufu8r
title: Waste-to-nutrition-Database.tab

description: The Excel file details the methodology and information gathered to complete the literature review
DOI: 10.48531/jbru.calmip/fem6n9/hows6v
title: Waste-to-nutrition-Supplementing-Information.pdf

description: Supplementing Information of the manuscript entitled: "Waste-to-nutrition: a review of current and emerging conversion pathways".
DOI: 10.48531/jbru.calmip/fem6n9/b6fekk
title: Waste-to-nutrition-Database_To-read.pdf

description: Description and additional information on the Waste-to-nutrition database
DOI: 10.48531/jbru.calmip/fem6n9/i25e0w
title: Waste-to-nutrition-Database.tab

description: The Excel file details the methodology and information gathered to complete the literature review
DOI: 10.48531/jbru.calmip/fem6n9/60xfbj
title: Waste-to-nutrition-Supplementing-Information.pdf

description: Supplementing Information of the manuscript entitled: "Waste-to-nutrition: a review of current and emerging conversion pathways".
DOI: 10.5281/zenodo.4621377
title: tRNS fMRI Experiment

description: Raw fMRI data collected during a trns-fMRI experiment. Each subject undewent two fMRI Session (Session1 and Session2). One anatomical and one resting state functional run were collected for each subject, and each session. Each subject folder (SnjN) contains data of one subject and it is subdived into two subfolder relative to each scanning session. 'SbjN_session1' includes raw data for pre-test session and is subdivided into one folder (SbjN_AnatomicalCNR') containing DICOM files for the anatomical CNR and for functional resting state data ('SbjN_RestingState'). 'Sbj_Session2' includes raw data collected during post-test session and is subdived into one foder ('SbjN_AnatomicalCNR) containing DICOM files for the anamotical CNR and one folder (SbjN_RestingState) containing DICOM files for the functional resting state data.
description: {"references": ["Cont\u00f2, F., Edwards, G., Tyler, S., Parrott, D., Grossman, E. D., & Battelli, L. (2020). Attention network modulation via tRNS correlates with attention gain. BioRxiv, 2020.09.15.298240. https://doi.org/10.1101/2020.09.15.298240"]}
DOI: 10.5281/zenodo.4621376
title: tRNS fMRI Experiment

description: Raw fMRI data collected during a trns-fMRI experiment. Each subject undewent two fMRI Session (Session1 and Session2). One anatomical and one resting state functional run were collected for each subject, and each session. Each subject folder (SnjN) contains data of one subject and it is subdived into two subfolder relative to each scanning session. 'SbjN_session1' includes raw data for pre-test session and is subdivided into one folder (SbjN_AnatomicalCNR') containing DICOM files for the anatomical CNR and for functional resting state data ('SbjN_RestingState'). 'Sbj_Session2' includes raw data collected during post-test session and is subdived into one foder ('SbjN_AnatomicalCNR) containing DICOM files for the anamotical CNR and one folder (SbjN_RestingState) containing DICOM files for the functional resting state data.
description: {"references": ["Cont\u00f2, F., Edwards, G., Tyler, S., Parrott, D., Grossman, E. D., & Battelli, L. (2020). Attention network modulation via tRNS correlates with attention gain. BioRxiv, 2020.09.15.298240. https://doi.org/10.1101/2020.09.15.298240"]}