cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4m94wgw6yh.1
title: Database FAR

description: Pancreatic surgery is one of the surgeries burdened with the highest mortality and morbidity rate. This is due both to the aggressive biological nature of the pathology affecting the organ and to the technical difficulties associated with surgery. A further aspect on which research is focusing is represented by inflammation related to oncological pathology. Inflammation plays an important role in tumor progression, and growing evidence has confirmed that the fibrinogen-to-albumin ratio (FAR) is an important prognostic factor for overall survival (OS) in malignant tumors. 
Inflammatory markers had demonstrated also a role in the prediction of postoperative complication after pancreatic surgery.
We speculate that FAR, as an easily available, cost-effective, and non-invasive prognostic indicator for pancreatic cancer patients, could help to identify patients at increased risk of complications such as postoperative pancreatic fistula (POPF). We therefore retrospectively analyzed the data relating to 117 pancreatic resections relating direct and indirect markers of inflammation with the incidence of post-operative complications.
DOI: 10.17632/t9h6p943xy.1
title: BEASC: Bangla emotional audio-speech corpus - An speech emotion recognition corpus for the Bangla language

description: BEASC is an audio-speech emotion recognition corpus for the Bangla language. The developed dataset consists of voice data from 34 speakers from diverse age groups between 19 to 57 (mean = 28.75 and Standard deviation = 9.346), equally balanced with 17 males and 17 females. This dataset contains 1224 speech-audio data of four emotional states. There are four emotional states recorded for three sentences. The three sentences are i. ‘১২ টা বেজে গেছে,’ ii. ‘আমি জানতাম এমন কিছু হবে’, and iii. ‘এ কেমন উপহার’. These emotional states include four basic human emotions: Angry, Happy, Sad, and Surprise. Three trials were preserved for each emotional expression. Hence, the total number of utterances involves three sentences × three repetitions × four emotions × 34 speakers = 1224 recordings. The format of the audio file is a . WAV format. We consider that happy and sad emotional speech has normal intensity and angry and surprise emotional states have a strong intensity. The data files are divided into 34 individual folders. Each folder contains 36 audio recordings of each participating actor. BEASC is a balanced dataset with 306 recordings of each individual emotion. The size of the BEASC dataset is 619 MB. While most of the existing datasets of different languages are recorded inside a closed studio or cover a single sentence, this dataset is collected by recording through smartphones, hence preserving the slightly noisy real-life environment. BEASC is compatible with various shallow machine learning and deep learning architectures such CNN, LSTM, HMM, Transformer, etc. Each data file has a unique filename. We followed the same procedure as the famous RAVDESS dataset for the naming. The filename consists of seven two-digit numerical identifiers, separated by hyphens (e.g., 03-01-01-01-02-02-02.wav). Each two-digit numerical identifier defines the level of a different experimental factor. The identifiers are ordered: Modality - Statement type - Emotion - Emotion Intensity - Statement - Repetition - Actor.wav. For example, the filename “03-01-01-01-02-02-02.wav” refers to: Audio only (03) - Scripted (01) - Happy (01) - Normal intensity (01) - 2nd Statement (02) - 2nd Repetition (02) - 2nd Actor, Female (02).
DOI: 10.48370/ofd/vfvvkd
title: Phlebia radiata Fr., 726

description: Fungarium Makromycetes
DOI: 10.5517/ccdc.csd.cc28z6l2
title: CCDC 2114404: Experimental Crystal Structure Determination

description: Related Article: Jelte Nimoth and Thomas Müller|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290l42
title: CCDC 2115723: Experimental Crystal Structure Determination

description: Related Article: Xin Jiang Feng|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290lpm
title: CCDC 2115740: Experimental Crystal Structure Determination

description: Related Article: Malik Williams|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290pmn
title: CCDC 2115831: Experimental Crystal Structure Determination

description: Related Article: Xiao Ma|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290qy0
title: CCDC 2115872: Experimental Crystal Structure Determination

description: Related Article: Junjun Tao|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290thn
title: CCDC 2115951: Experimental Crystal Structure Determination

description: Related Article: Richard Jack Lyons, Thomas Fellowes|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290wnw
title: CCDC 2116018: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290wpx
title: CCDC 2116019: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290wqy
title: CCDC 2116020: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290wrz
title: CCDC 2116021: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290ws0
title: CCDC 2116022: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc290wt1
title: CCDC 2116023: Experimental Crystal Structure Determination

description: Related Article: Sampath B. Alahakoon, Gregory T. McCandless, and Ronald A. Smaldone|2021|CSD Communication|||
DOI: 10.5519/dkrhpxjh
title: Lepidosaur bite-force data

description: ## Data from paper: Clade-wide variation in bite-force performance is determined primarily by size not ecology. https://doi.org/10.1098/rspb.2021.2493 &gt; Justin E Isip, Marc EH Jones and Natalie Cooper. 2022. Clade-wide variation in bite-force performance is determined primarily by size not ecology. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2021.2493. These are the data required for the analyses. Full metadata are provided for the raw data, and the data used to build the sub-datasets for each analysis. All are included here for completeness. For more details see https://github.com/nhcooper123/lepidosaur-biteforce. ## Additional data The ecological data are all from Meiri et al (2018). **Please cite this paper if you use these data!** &gt; Meiri, S. Traits of lizards of the world: Variation around a successful evolutionary design. Global Ecol Biogeogr. 2018; 27: 1168– 1172. https://doi.org/10.1111/geb.12773 The phylogeny comes from Wright et al (2015). **Please cite this paper if you use these data!** &gt; Wright, AM, Lyons, KM, Brandley, MC, Hillis, DM. 2015. Which came first: the lizard or the egg? Robustness in phylogenetic reconstruction of ancestral states. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 504– 516.
DOI: 10.5281/zenodo.5961197
title: Dataset and analyses for : Can foot placement during gait be trained? Adaptations in stability control when ankle moments are constrained

description: Here you'll find the dataset and analyses belonging to the publication "Can foot placement during gait be trained? Adaptations in stability control when ankle moments are constrained". Participants walked on a treadmill with and without shoes constraining ankle moment control. We evaluated whether participants improved their degree of foot placement control, during and after walking with ankle moment constraints. Ground reaction forces and full body kinematics have been collected. For more detail on the folders' contents please see "Read me - Can foot placement during gait be trained.docx".
DOI: 10.5281/zenodo.5961196
title: Dataset and analyses for : Can foot placement during gait be trained? Adaptations in stability control when ankle moments are constrained

description: Here you'll find the dataset and analyses belonging to the publication "Can foot placement during gait be trained? Adaptations in stability control when ankle moments are constrained". Participants walked on a treadmill with and without shoes constraining ankle moment control. We evaluated whether participants improved their degree of foot placement control, during and after walking with ankle moment constraints. Ground reaction forces and full body kinematics have been collected. For more detail on the folders' contents please see "Read me - Can foot placement during gait be trained.docx".
DOI: 10.5519/qd.1ywbst82
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (433 records)
DOI: 10.5281/zenodo.5970811
title: Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex

description: These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.
DOI: 10.5281/zenodo.5970810
title: Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex

description: These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.
DOI: 10.5281/zenodo.5947174
title: Infection Delay Project Data

description: GitHub Repository for the project code can be found here: https://github.com/shivyucel/infection-delay-project The raw data folder contains the census and commuting data, along with their relevant shapefiles and labels. It also contains the shapefiles for the 2599 hexagons used in this analysis. The prelim_data folder contains the h3_IDs table, used to numerically identify hexagons (more info included in info file in folder). It also includes the commuting data, already preprocessed using code in the Effective Distance/SIR Model folder to filter out regions which do not have suitable data. The SIR_model_inputs folder has the mobility matrix based on the commuting data, and the population data (both already filtered to include suitable regions, same as above). The infection_delay_inputs folder contains the baseline and real mobility effective distance matrices. The mobility_data folder contains the raw mobility data from March - September 2020 (raw_mobility_data.csv), the mobility reductions across all hexagons with data around the lockdown (mobility_reduction.csv), and the mobility change (filtered_mobility_reduction.csv) used in the effective_distance_real_changes.py code, filtered for the regions which are suitable for the effective distance/SIR calculations. The result_data folder contains the tables used to generate the results, including the weighted median 10-day infection delay values for every region, the outbreak divided results, merged with income and centrality data. The final data files are 'weighted_hexagon_data.csv' and 'longford_outbreak_split_delays'. NOTE: The infection delay files take up 30+ GB of storage, and are not included here. In both mobility scenario, 2599 tables are generated, each with 2599 columns, representing the time series of every hexagon for every outbreak scenario. These are then duplicated in reorient_infection_delay_tables.py, to make them amenable to analysis. Full commuting and census data, beyond that included in the raw data folder, can be found here (https://www.metro.sp.gov.br/pesquisa-od/) and here (https://censo2010.ibge.gov.br/resultados/resumo.html), respectively.
DOI: 10.5281/zenodo.5947173
title: Infection Delay Project Data

description: GitHub Repository for the project code can be found here: https://github.com/shivyucel/infection-delay-project The raw data folder contains the census and commuting data, along with their relevant shapefiles and labels. It also contains the shapefiles for the 2599 hexagons used in this analysis. The prelim_data folder contains the h3_IDs table, used to numerically identify hexagons (more info included in info file in folder). It also includes the commuting data, already preprocessed using code in the Effective Distance/SIR Model folder to filter out regions which do not have suitable data. The SIR_model_inputs folder has the mobility matrix based on the commuting data, and the population data (both already filtered to include suitable regions, same as above). The infection_delay_inputs folder contains the baseline and real mobility effective distance matrices. The mobility_data folder contains the raw mobility data from March - September 2020 (raw_mobility_data.csv), the mobility reductions across all hexagons with data around the lockdown (mobility_reduction.csv), and the mobility change (filtered_mobility_reduction.csv) used in the effective_distance_real_changes.py code, filtered for the regions which are suitable for the effective distance/SIR calculations. The result_data folder contains the tables used to generate the results, including the weighted median 10-day infection delay values for every region, the outbreak divided results, merged with income and centrality data. The final data files are 'weighted_hexagon_data.csv' and 'longford_outbreak_split_delays'. NOTE: The infection delay files take up 30+ GB of storage, and are not included here. In both mobility scenario, 2599 tables are generated, each with 2599 columns, representing the time series of every hexagon for every outbreak scenario. These are then duplicated in reorient_infection_delay_tables.py, to make them amenable to analysis. Full commuting and census data, beyond that included in the raw data folder, can be found here (https://www.metro.sp.gov.br/pesquisa-od/) and here (https://censo2010.ibge.gov.br/resultados/resumo.html), respectively.
DOI: 10.5525/gla.researchdata.1241
title: Quantum microscopy based on Hong-Ou-Mandel interference

description: Data relevant to publication with folders containing measurement data used to create figures plus matlab code. Please use the 'Request Data' button to be sent a link to download the data.
DOI: 10.5519/qd.z1f7n8al
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (439 records)
DOI: 10.6084/m9.figshare.18093830
title: Supplementary Material for: Risk and Prognosis of Subsequent Primary Gastric Cancer

description: Introduction: The incidence and prognostic impact of subsequent primary gastric cancer (GC) in a population of other cancer survivors is unclear. We aimed to evaluate susceptibility to subsequent primary GC in cancer survivors and prognosis of GC with prior cancer history. Methods: 2,211 and 23,416 GC cases with and without prior cancer history were retrospectively selected from the Surveillance, Epidemiology and End Results (SEER) database. Potential risk of developing subsequent primary GC was assessed through standardized incidence ratios (SIRs). Cox regression were adopted to analyze the influence of prior cancer history and clinical characteristic factors on the prognosis of subsequent primary GC. A nomogram was established to predict overall survival (OS). Propensity score matching (PSM) was conducted to eliminate possible bias. Results: Compared with general population, cancer survivors had an increased risk of subsequent primary GC (SIR 1.17, 95% CI 1.15-1.20, P&lt;0.05). Prior cancer history was related to poor OS of GC [adjusted hazard ratio (aHR) 1.12, 95% CI 1.06-1.19, P&lt;0.001], but not cancer-specific survival (aHR 0.97, 95% CI 0.89-1.05, P=0.441). In addition, age, grade, stage, year of diagnosis, surgery, TNM stage and tumor size were independent prognostic factors for OS in GC cases with prior cancers. The concordance index of the nomogram was 0.72 (95% CI 0.71-0.74), and calibrate curves showed good agreement between prediction by the nomogram and actual observation. Conclusions: Cancer survivors with increased risk of developing subsequent primary GC should strengthen their monitoring and follow-up to prevent occurrence of subsequent primary gastric cancer.
DOI: 10.5523/bris.i56djt07k3gx2m0o6hcjkzwje
title: A fossilised ventral ganglion reveals a chaetognath affinity for Cambrian nectocaridids

description: This folder contains supporting data accompanying study of a new taxon from Sirius Passet, Nektognathus evasmithae.
DOI: 10.6084/m9.figshare.19074509.v2
title: Data of Research on Desktop Object Grasping Based on Ellipse Fitting

description: Data of Research on Desktop Object Grasping Based on Ellipse Fitting
DOI: 10.5281/zenodo.6008862
title: GALA: a computational framework for de novo chromosome-by-chromosome assembly with long reads

description: C.elegans, O.sativa and Human assemblies produced by GALA software
DOI: 10.5281/zenodo.3840273
title: GALA: a computational framework for de novo chromosome-by-chromosome assembly with long reads

description: C.elegans, O.sativa and Human assemblies produced by GALA software
DOI: 10.5255/ukda-sn-855392
title: London Stakeholders on London Governance, 2020-2021

description: The dataset consists of semi-structured interviews with governance stakeholders in London: representatives of London boroughs, the Greater London Authority, business organisations and non-governmental organisations. The interviews focused on: how the governance system in London works, how individual actors responded to the Covid-19 pandemic, how the pandemic influenced Londoners’ local socio-economic status, and how individual actors envision an ideal London governance.
DOI: 10.6084/m9.figshare.19137519
title: Seismic geomorphology of submarine landslides in the Chalk Group of the Danish Central Graben: implications for reservoir potential

description: The Python script that calculates the bearing between two points (start and end point of the long axis) and visualizes the orientations using the Python library mplstereonet (https://github.com/joferkington/mplstereonet)
DOI: 10.6084/m9.figshare.19137519.v1
title: Seismic geomorphology of submarine landslides in the Chalk Group of the Danish Central Graben: implications for reservoir potential

description: The Python script that calculates the bearing between two points (start and end point of the long axis) and visualizes the orientations using the Python library mplstereonet (https://github.com/joferkington/mplstereonet)
DOI: 10.25747/96kp-5033
title: An Annotated and Classified Maritime Dataset aimed at Machine Learning
DOI: 10.25403/upresearchdata.19137083
title: The relationship between the lingual position of the mandibular molar roots and the floor of the mouth

description: Data is collected in three age groups, and in each age group the type of molar (1st, 2nd, and 3rd molar). The data is further divided into male and female under each molar type, and into for each subject into left and right. Then the data for intra- and inter-observer errors is also included. Datasets are in MicroSoft Excel Spreadsheet format.<br>
DOI: 10.25403/upresearchdata.19137083.v1
title: The relationship between the lingual position of the mandibular molar roots and the floor of the mouth

description: Data is collected in three age groups, and in each age group the type of molar (1st, 2nd, and 3rd molar). The data is further divided into male and female under each molar type, and into for each subject into left and right. Then the data for intra- and inter-observer errors is also included. Datasets are in MicroSoft Excel Spreadsheet format.<br>
DOI: 10.5281/zenodo.6006178
title: Raw data of all figures

description: Raw data of all figures
DOI: 10.5517/ccdc.csd.cc23xmc1
title: CCDC 1963923: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Klaus H. Theopold|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23xmd2
title: CCDC 1963924: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Klaus H. Theopold|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24vjh1
title: CCDC 1991703: Experimental Crystal Structure Determination

description: Related Article: Mohammad Shahwaz Ahmad, Mohd Khalid, Mohammad Shahnawaz Khan, Mohammad Shahid, Musheer Ahmad, Haris Saeed, Mohammad Owais, Mo Ashafaq|2021|Appl.Organomet.Chem.|35|e6221|doi:10.1002/aoc.6221
DOI: 10.15129/170add09-19b9-4968-a257-3fb20529175f
title: Data for: "ConcrEITS: an electrical impedance interrogator for concrete damage detection using self-sensing repairs"

description: Raw data: 16 electrode EIT current measurements on six small scale samples undergoing linear displacement until cracking. Data form is described in the ReadMe file.
DOI: 10.15131/shef.data.16629064
title: Correlative Super-Resolution Optical and Atomic Force Microscopy Reveals Relationships Between Bacterial Cell Wall Architecture and Synthesis in Bacillus subtilis - Raw data repository

description: This is the raw data corresponding to the article published in ACS Nano by the authors. <br><br>
DOI: 10.15131/shef.data.16629064.v1
title: Correlative Super-Resolution Optical and Atomic Force Microscopy Reveals Relationships Between Bacterial Cell Wall Architecture and Synthesis in Bacillus subtilis - Raw data repository

description: This is the raw data corresponding to the article published in ACS Nano by the authors. <br><br>
DOI: 10.5281/zenodo.5947668
title: Lipidomics in Rett Syndrome Patients

description: Lipidomics analysis of CSF and plasma samples obtained from Rett syndrome (RS) patients and controls using UHPLC-MS/MS. Raw data (Panasonic raw image) obtained from analysis in positive and negative ionisation mode. Processed results files from each sample are shown in separate excel files. Enclosed meta file provides sample information.
DOI: 10.5281/zenodo.5947667
title: Lipidomics in Rett Syndrome Patients

description: Lipidomics analysis of CSF and plasma samples obtained from Rett syndrome (RS) patients and controls using UHPLC-MS/MS. Raw data (Panasonic raw image) obtained from analysis in positive and negative ionisation mode. Processed results files from each sample are shown in separate excel files. Enclosed meta file provides sample information.
DOI: 10.5281/zenodo.5998022
title: COMPRISE_Data11_M-NER_V1.0

description: A named entity recognition (NER) dataset (a subset of the News dataset). It has four entity types: Personal names, Locations, Organizations, and Dates. The dataset supports ten African languages: Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Luo, Nigerian-Pidgin, Swahili, Wolof, and Yoruba.
DOI: 10.5281/zenodo.5998021
title: COMPRISE_Data11_M-NER_V1.0

description: A named entity recognition (NER) dataset (a subset of the News dataset). It has four entity types: Personal names, Locations, Organizations, and Dates. The dataset supports ten African languages: Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Luo, Nigerian-Pidgin, Swahili, Wolof, and Yoruba.