cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3967246
title: Results files of PRosettac for the following PDBs: 5T35, 6BOY, 6BN7, 6BN8, 6BN9, 6BNB, 6HAX, 6HAY, 6HR2, 6SIS.

description: This depository includes supplementary files from the manuscript "PRosettaC: Rosetta based modeling of PROTAC mediated ternary complexes". Link for the paper pre-print: https://www.biorxiv.org/content/10.1101/2020.05.27.119354v1 The code for the protocol is available at https://github.com/LondonLab/PRosettaC A web server for applying the protocol by a simple user interface will soon be available at http://prosettac.weizmann.ac.il Content of this directory: The final models of the train and test sets from the manuscript. The unsuccessful unbound results for the train set. The input files for the bound results. The models for the CRBN-BTK PROTACs 4-11. The best RMSD models of the train set, together with their native models after renumbering their residues for protein-protein interaction analysis. PDBs of bound structures: 5T35, 6BOY, 6BN7, 6BN8, 6BN9, 6BNB, 6SIS, 6HAX, 6HAY, 6HR2. PDBs of unbound structures: 6BOY, 6BN7, 6BN9, 5T35.
DOI: 10.5281/zenodo.3925438
title: Results files of PRosettac for the following PDBs: 5T35, 6BOY, 6BN7, 6BN8, 6BN9, 6BNB, 6HAX, 6HAY, 6HR2, 6SIS.

description: This depository includes supplementary files from the manuscript "PRosettaC: Rosetta based modeling of PROTAC mediated ternary complexes". Link for the paper pre-print: https://www.biorxiv.org/content/10.1101/2020.05.27.119354v1 The code for the protocol is available at https://github.com/LondonLab/PRosettaC A web server for applying the protocol by a simple user interface will soon be available at http://prosettac.weizmann.ac.il Content of this directory: The final models of the train and test sets from the manuscript. The unsuccessful unbound results for the train set. The input files for the bound results. The models for the CRBN-BTK PROTACs 4-11. The best RMSD models of the train set, together with their native models after renumbering their residues for protein-protein interaction analysis. PDBs of bound structures: 5T35, 6BOY, 6BN7, 6BN8, 6BN9, 6BNB, 6SIS, 6HAX, 6HAY, 6HR2. PDBs of unbound structures: 6BOY, 6BN7, 6BN9, 5T35.
DOI: 10.5281/zenodo.3967296
title: Data related to Sasse et al. (2020) "Regional impacts of electricity system transition in Central Europe until 2035"

description: This is the accompanying dataset for the publication "Regional impacts of electricity system transition in Central Europe until 2035". The dataset contains the following input data files: <strong>Capacity_Factor_EXPANSE.csv</strong>: annual capacity factors per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh/MWh <strong>Demand_EXPANSE.csv</strong>: annual electricity demand per NUTS-3 region, used by the EXPANSE model, in MWh and in TWh <strong>LCOE_EXPANSE.csv</strong>: levelized electricity generation costs per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in EUR/MWh <strong>Emax_EXPANSE.csv</strong>: maximum annual electricity generation potential per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh <strong>Emin_EXPANSE.csv</strong>: minimum annual electricity generation potential per NUTS-3 region and electricity generation technology, used by the EXPANSE model, in MWh <strong>Costs_PyPSA.csv</strong>: Cost assumptions, used by the PyPSA model, units defined in units column The dataset contains the result data files: <strong>Aggregated_Impacts.csv</strong>: Aggregated impacts for all MGA scenarios and distinct scenarios, units defined in each column <strong>Regional_Impacts.csv</strong>: Regional impacts for all distinct scenarios, units defined in each column
DOI: 10.5281/zenodo.3961933
title: Deleted

description: This dataset has moved to http://doi.org/10.5281/zenodo.3967297
DOI: 10.5281/zenodo.3952445
title: Deleted

description: This dataset has moved to http://doi.org/10.5281/zenodo.3967297
DOI: 10.5281/zenodo.3954924
title: Deleted

description: This dataset has been moved.
DOI: 10.5281/zenodo.3967290
title: SpaGE Spatial Gene Enhancement using scRNA-seq

description: Spatial transcriptomics and scRNA-seq datasets using for integration and prediction of spatially non-measured genes using SpaGE
DOI: 10.5281/zenodo.3952446
title: Deleted

description: This dataset has been moved.
DOI: 10.21227/4grd-kv29
title: Alarm Logs from Packaging Industry (ALPI)

description: The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly. All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).
DOI: 10.21227/34tq-pe30
title: Alarm Logs from Packaging Industry (ALPI)

description: The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly. All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).
DOI: 10.21227/c191-ng61
title: Alarm Logs from Packaging Industry (ALPI)

description: The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly. All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).
DOI: 10.5066/p9ur3kwx
title: 2012 Geomagnetic Repeat Station Data

description: This data release consists of Magnetic Repeat Station data collected at three different sites in the central United States. One site is in Fremont, NE, the other two are in Hereford and Del Rio, TX. Repeat Stations are temporary magnetic installations, where the Earth's magnetic field, or geomagnetic field, is measured for several days, with the goal of producing mean values of the vector components of the field, when the geomagnetic field is in a quiescent state. The data consist of approximately 72 hours of the vector magnetic field variations at each site plus calibration data measured periodically during the collection of the time series data. The time series data were digitally recorded using a 3-axis Narod fluxgate magnetometer and GEM GSM-19 overhauser magnetometer. The data were stored in files that could be used in a spreadsheet. The calibration data needed to derive absolute data were recorded manually using a Declination-Inclination magnetometer. Absolute and baseline values were computed using spreadsheets.
DOI: 10.21227/fkca-1p70
title: A Batch of Integer Data Sets for Clustering Algorithms

description: This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
DOI: 10.21227/mrqr-vb44
title: A Batch of Integer Data Sets for Clustering Algorithms

description: This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
DOI: 10.21227/9g6h-r174
title: Alarm Logs in Packaging Industry (ALPI)

description: The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly. All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).
DOI: 10.5523/bris.pji8xwa0q6ue27lcu8gp62k0q
title: Data from Study_How does complexity affect the successful engineering of systems throughout a system lifecycle

description: This dataset contains six documents (pdf) detailing the set up and results of a research study entitled "How does complexity affect the successful engineering of systems throughout a system lifecycle?". This study involved semi-structured interviews with personnel from Thales Group concerning the utility of a particular decision-support tool, conducted between 1st April 2019 and 3rd September 2019.
DOI: 10.21227/6xxs-vw78
title: A Batch of Integer Data Sets for Clustering Algorithms

description: This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
DOI: 10.5281/zenodo.3956666
title: Dagstuhl ChoirSet

description: Dagstuhl ChoirSet is a multitrack dataset of a cappella choral music designed to support MIR research on choral singing. The dataset includes recordings of an amateur vocal ensemble performing two choir pieces in full choir and quartet settings. The audio data was recorded during an MIR seminar at Schloss Dagstuhl using different close-up microphones to capture the individual singers’ voices. A detailed documentation of this dataset can be found in: Sebastian Rosenzweig, Helena Cuesta, Christof Weiß, Frank Scherbaum, Emilia Gómez, and Meinard Müller:<br> <strong>Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing</strong>.<br> Transactions of the International Society for Music Information Retrieval, 3(1), pp. 98–110, 2020.<br> DOI: https://doi.org/10.5334/tismir.48
DOI: 10.21227/1cgg-v030
title: A Batch of Integer Data Sets for Clustering Algorithms

description: This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
DOI: 10.5281/zenodo.3967034
title: Hydrolysable carbohydrate data collected from the trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> Hydrolysable carbohydrate (referred to as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon that is excreted by most (micro)organisms or released by continental margins/sediments. It is a carbon source for heterotrophic bacteria. These carbohydrates could also potentially bind iron and act as an iron binding ligand. This data is used to explore the nature of iron ligands and relate to biological and chemical oceanography. <strong>Dataset contents</strong> ace_hydrolysable_carbohydrates_tpzt_data.csv, data file, comma-separated values ace_hydrolysable_carbohydrates_tpzt_data_visual_summary.png, metadata, portable network graphics README.txt, metadata, text format data_file_header.txt, metadata, text format change_log.txt <strong>Dataset license</strong> This hydrolysable carbohydrate dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ <strong>Change log</strong> v1.1 - permissions changed to open access (CC BY 4.0 license) and small changes add license to README.txt format of data_file_header.txt add Frictionless Data schema files v1.0 - initial release of dataset
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. This work was also supported by Swiss National Science Foundation grants PP00P2_138955 and PP00P2_166197.
description: {"references": ["Myklestad S, Sk\u00e5n\u00f8y E, Hestmann S, A sensitive and rapid method for analysis of dissolved mono- and polysaccharides in seawater. Mar Chem 56:279\u2013286 (1997)", "Ocean Data View (ver. 4.5). Schlitzer,R.(2012). OceanData View (version4.5)"]}
DOI: 10.5281/zenodo.2635685
title: Hydrolysable carbohydrate data collected from the trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> Hydrolysable carbohydrate (referred to as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon that is excreted by most (micro)organisms or released by continental margins/sediments. It is a carbon source for heterotrophic bacteria. These carbohydrates could also potentially bind iron and act as an iron binding ligand. This data is used to explore the nature of iron ligands and relate to biological and chemical oceanography. <strong>Dataset contents</strong> ace_hydrolysable_carbohydrates_tpzt_data.csv, data file, comma-separated values ace_hydrolysable_carbohydrates_tpzt_data_visual_summary.png, metadata, portable network graphics README.txt, metadata, text format data_file_header.txt, metadata, text format change_log.txt <strong>Dataset license</strong> This hydrolysable carbohydrate dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ <strong>Change log</strong> v1.1 - permissions changed to open access (CC BY 4.0 license) and small changes add license to README.txt format of data_file_header.txt add Frictionless Data schema files v1.0 - initial release of dataset
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. This work was also supported by Swiss National Science Foundation grants PP00P2_138955 and PP00P2_166197.
description: {"references": ["Myklestad S, Sk\u00e5n\u00f8y E, Hestmann S, A sensitive and rapid method for analysis of dissolved mono- and polysaccharides in seawater. Mar Chem 56:279\u2013286 (1997)", "Ocean Data View (ver. 4.5). Schlitzer,R.(2012). OceanData View (version4.5)"]}
DOI: 10.34740/kaggle/dsv/1383472
title: COVID-19 Indonesia Dataset
DOI: 10.5523/bris.3a0fi24vr7yp52jx88c3a9vzp6
title: 20190611-SurveyDesignEngineerSystemComplexityFactors

description: This dataset contains the questions used in the survey entitled "When we attempt to design and engineer a novel system, which aspects of the system’s complexity are most significant for the project’s success or failure?", distributed to members of the International Council of Systems Engineers (INCOSE) between 11th March 2019 to 10th June 2019. The data is used to contribute towards a PhD in Systems Engineering being carried out at the University of Bristol as part of an Engineering and Physical Sciences Research Council (EPSRC) funded project (iCASE 16000139) entitled `Ensuring Flexible Design Strategies for Complex Systems Engineering'.