cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13238801
title: Figure4 data

description: Surface O3, vertical profiles of potential temperature and O3 from observations, control and sensitive simulations
DOI: 10.6084/m9.figshare.13238801.v1
title: Figure4 data

description: Surface O3, vertical profiles of potential temperature and O3 from observations, control and sensitive simulations
DOI: 10.6084/m9.figshare.13238807
title: Figure5 data

description: Vertical profiles of potential temperature and O3 during simulated period.
DOI: 10.6084/m9.figshare.13238807.v2
title: Figure5 data

description: Vertical profiles of potential temperature and O3 during simulated period.
DOI: 10.6084/m9.figshare.13238807.v1
title: Untitled Item

description: Vertical profiles of potential temperature and O3 during simulated period.
DOI: 10.21227/a1aj-a878
title: Team14_5C_2020_SoCSE_KLETech(1)

description: Energy consumption is predicted for 3248 households using smart meter data.
DOI: 10.5281/zenodo.4274495
title: Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods

description: Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this. In the case of floods, for instance, data collection from social media can be triggered by a severe weather forecast and/or a flood prediction. By way of contrast, in this paper we explore the possibility of having an entirely independent flood monitoring system which is based completely on social media, and which is completely self-activated. This independence and self-activation would bring increased robustness, as the system would not depend on other mechanisms for forecasting. We observe that social media can indeed help in the early detection of some flood events that would otherwise not be detected until later, albeit at the cost of many false positives. Overall, our experiments suggest that social media signals should only be used to complement existing monitoring systems, and we provide various explanations to support this argument. This dataset contains the jupyter notebook and related data for running experiments described in the paper along with an additional table with full list of events considered
DOI: 10.5281/zenodo.4274494
title: Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods

description: Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this. In the case of floods, for instance, data collection from social media can be triggered by a severe weather forecast and/or a flood prediction. By way of contrast, in this paper we explore the possibility of having an entirely independent flood monitoring system which is based completely on social media, and which is completely self-activated. This independence and self-activation would bring increased robustness, as the system would not depend on other mechanisms for forecasting. We observe that social media can indeed help in the early detection of some flood events that would otherwise not be detected until later, albeit at the cost of many false positives. Overall, our experiments suggest that social media signals should only be used to complement existing monitoring systems, and we provide various explanations to support this argument. This dataset contains the jupyter notebook and related data for running experiments described in the paper along with an additional table with full list of events considered
DOI: 10.5517/ccdc.csd.cc253wqx
title: CCDC 1999739: Experimental Crystal Structure Determination

description: Related Article: Richard D. J. Lunn, Derek A. Tocher, Philip J. Sidebottom, Mark G. Montgomery, Adam C. Keates, Claire J. Carmalt|2020|Cryst.Growth Des.|20|7238|doi:10.1021/acs.cgd.0c00901
DOI: 10.5517/ccdc.csd.cc253ww2
title: CCDC 1999744: Experimental Crystal Structure Determination

description: Related Article: Richard D. J. Lunn, Derek A. Tocher, Philip J. Sidebottom, Mark G. Montgomery, Adam C. Keates, Claire J. Carmalt|2020|Cryst.Growth Des.|20|7238|doi:10.1021/acs.cgd.0c00901
DOI: 10.5517/ccdc.csd.cc25sqf4
title: CCDC 2019756: Experimental Crystal Structure Determination

description: Related Article: Jean Lombard, Tanya le Roex, Delia A. Haynes|2020|Cryst.Growth Des.|20|7384|doi:10.1021/acs.cgd.0c01054
DOI: 10.21227/22x3-3478
title: Monthly Energy Prediction from Smart Meter Data

description: Datasets provided:
DOI: 10.21227/d52k-0f78
title: AI Boffins

description: Timeseries is a sequence taken at successive equally spaced points in time. Rise and fall of stock prices, weather etc. are all different examples of timeseries. Due to its property of persistence, timeseries observations change slowly. Also, past observations provide information about current observations.EON[1] wants to predict a customer’s annual consumption with lesser amount of data as a fairly new technology of smart meters has less historical data. This will help them configure direct debit payments and help in achieving timely payments.The goal is to predict monthly electricity consumption for 3248 households in the coming year (January to December) using historical half hourly energy readings of 3248 smart meters . The half hourly data from EONwas converted to daily data and missing values were imputed. Data was visualized using TSNE and Clustering. Robust STL and Dickey Fuller tests were performed to analyze the seasonality and trend. Due to stationary nature of the data, KNN model was used to obtain the consumption for 3248 meter-ids for the coming year.
DOI: 10.5281/zenodo.4274519
title: Materialien zum VDB-Workshop im Dezember 2021: Der Datenmanagementplan als Teil eines Förderantrags und wie Bibliotheken dabei forschungsnah unterstützen.

description: Patrick Hochstenbach who has drawn over 100 icons and cartoons to illustrate the Open Science training handbook Large ZIP archive of PNG graphics (1.5Mb) Download the 16 cartoons, e.g. fundamental rules of open science here: ZIP archive of 16 PNG illustrations (15Mb) has drawn 2 illustrations for the VDB workshop in December 2020: https://www.vdb-online.org/veranstaltungen/891/ Programm: https://www.vdb-online.org/veranstaltungen/891/Forschungsnahe_Dienste_zum_Laufen_bringen.pdf Materialien zum Workshop, inkl. der Illustrationen.
DOI: 10.5281/zenodo.4274518
title: Materialien zum VDB-Workshop im Dezember 2021: Der Datenmanagementplan als Teil eines Förderantrags und wie Bibliotheken dabei forschungsnah unterstützen.

description: Patrick Hochstenbach who has drawn over 100 icons and cartoons to illustrate the Open Science training handbook Large ZIP archive of PNG graphics (1.5Mb) Download the 16 cartoons, e.g. fundamental rules of open science here: ZIP archive of 16 PNG illustrations (15Mb) has drawn 2 illustrations for the VDB workshop in December 2020: https://www.vdb-online.org/veranstaltungen/891/ Programm: https://www.vdb-online.org/veranstaltungen/891/Forschungsnahe_Dienste_zum_Laufen_bringen.pdf Materialien zum Workshop, inkl. der Illustrationen.
DOI: 10.21227/pmds-9t42
title: Monthly Energy Prediction from Smart Meter Data

description: Datasets provided: 1. Consumption.csv:Half hourly consumption in "kwh" of "" meters for a year is given.Null values %: 51.6%2. Weather-avg/min/max.csv:Per day average/minimum/maximum temperature of meter for a year is given.3. AddInfo.csv:Some additional information for some households is provided but more than 95% values are null. The attributes "dwelling_type" and "no. of bedrooms" holds good amount of data with 24% and 13% null values respectively. Data Preprocessing:Firstly, We assigned a number to each meter id and dropped meter ids from the dataframe so that none of our columns remain of object dtype.Treating null values:We had a huge amount of null values in our dataset. Prediction in the presence of null values is highly inappropriate hence, treating null values is must.On observing the dataset we found out that for huge number of meter ids data from January to September was missing. Predicting values for those month can be a challenge.We dropped columns with more than 95% null values then transposed the dataset (Time frame now appear as row names and meters are columns). Later we converted the time into date-time format and performed data smoothening by treating outliers using interquartile range.Then we computed mean values of consumption by each meter in each month and performed interpolation on weather data to treat null values.Now we have an average value of consumption for each month by each meter but the months with null values for entire month are still not treated well.Therefore, for the meters facing unavailability of data for few months are compared to those holding values for those months. For example, consider meter1 and meter2 such that for meter 1, January to September data is unavailable and for meter 2 data is available for all the months. Now we compared the data for October, November and December of both the meters. If they are somewhat similar then we filled the data of meter 1 using the data of meter2 for January to September since their consuming patterns are similar. This was purely a trial and error. Modelling:Classified train and test data.About 80% of the data is train data and 20% is our test data.Then we verified whether our data is yet stationary or not using Dickey Fuller method. Since our dataset doesn't seem stationary by observing the trends so we used differencing method and stationarized the data.At the end we applied ARIMA on our dataset for predicting monthly consumption of each meter.
DOI: 10.21227/vk5a-8q59
title: Monthly Energy Prediction from Smart Meter Data

description: Datasets provided: 1. Consumption.csv:Half hourly consumption in "kwh" of "" meters for a year is given.Null values %: 51.6%2. Weather-avg/min/max.csv:Per day average/minimum/maximum temperature of meter for a year is given.3. AddInfo.csv:Some additional information for some households is provided but more than 95% values are null. The attributes "dwelling_type" and "no. of bedrooms" holds good amount of data with 24% and 13% null values respectively. Data Preprocessing:Firstly, We assigned a number to each meter id and dropped meter ids from the dataframe so that none of our columns remain of object dtype.Treating null values:We had a huge amount of null values in our dataset. Prediction in the presence of null values is highly inappropriate hence, treating null values is must.On observing the dataset we found out that for huge number of meter ids data from January to September was missing. Predicting values for those month can be a challenge.We dropped columns with more than 95% null values then transposed the dataset (Time frame now appear as row names and meters are columns). Later we converted the time into date-time format and performed data smoothening by treating outliers using interquartile range.Then we computed mean values of consumption by each meter in each month and performed interpolation on weather data to treat null values.Now we have an average value of consumption for each month by each meter but the months with null values for entire month are still not treated well.Therefore, for the meters facing unavailability of data for few months are compared to those holding values for those months. For example, consider meter1 and meter2 such that for meter 1, January to September data is unavailable and for meter 2 data is available for all the months. Now we compared the data for October, November and December of both the meters. If they are somewhat similar then we filled the data of meter 1 using the data of meter2 for January to September since their consuming patterns are similar. This was purely a trial and error. Modelling:Classified train and test data.About 80% of the data is train data and 20% is our test data.Then we verified whether our data is yet stationary or not using Dickey Fuller method. Since our dataset doesn't seem stationary by observing the trends so we used differencing method and stationarized the data.At the end we applied ARIMA on our dataset for predicting monthly consumption of each meter.
DOI: 10.21227/8cw5-tc40
title: Monthly Energy Prediction from Smart Meter Data

description: Datasets provided: 1. Consumption.csv:Half hourly consumption in "kwh" of "" meters for a year is given.Null values %: 51.6%2. Weather-avg/min/max.csv:Per day average/minimum/maximum temperature of meter for a year is given.3. AddInfo.csv:Some additional information for some households is provided but more than 95% values are null. The attributes "dwelling_type" and "no. of bedrooms" holds good amount of data with 24% and 13% null values respectively. Data Preprocessing:Firstly, We assigned a number to each meter id and dropped meter ids from the dataframe so that none of our columns remain of object dtype.Treating null values:We had a huge amount of null values in our dataset. Prediction in the presence of null values is highly inappropriate hence, treating null values is must.On observing the dataset we found out that for huge number of meter ids data from January to September was missing. Predicting values for those month can be a challenge.We dropped columns with more than 95% null values then transposed the dataset (Time frame now appear as row names and meters are columns). Later we converted the time into date-time format and performed data smoothening by treating outliers using interquartile range.Then we computed mean values of consumption by each meter in each month and performed interpolation on weather data to treat null values.Now we have an average value of consumption for each month by each meter but the months with null values for entire month are still not treated well.Therefore, for the meters facing unavailability of data for few months are compared to those holding values for those months. For example, consider meter1 and meter2 such that for meter 1, January to September data is unavailable and for meter 2 data is available for all the months. Now we compared the data for October, November and December of both the meters. If they are somewhat similar then we filled the data of meter 1 using the data of meter2 for January to September since their consuming patterns are similar. This was purely a trial and error. Modelling:Classified train and test data.About 80% of the data is train data and 20% is our test data.Then we verified whether our data is yet stationary or not using Dickey Fuller method. Since our dataset doesn't seem stationary by observing the trends so we used differencing method and stationarized the data.At the end we applied ARIMA on our dataset for predicting monthly consumption of each meter.
DOI: 10.21227/a1wa-1e88
title: Team18_5E_2020_SoCSE_KLETech

description: IEEE-CIS TECHNICAL CHALLENGE ON ENERGY PREDICTION FROM SMART METER DATA
DOI: 10.5281/zenodo.4274090
title: ACF database on the predictors of time to recovery and non-response to SAM treatment in the MANGO trial

description: This database contains the variables used to analyse the predictors of time to recovery and non-response to treatment of SAM in Burkina Faso. These results are have been submitted to review in Plos One in November 2020.
DOI: 10.5281/zenodo.4274089
title: ACF database on the predictors of time to recovery and non-response to SAM treatment in the MANGO trial

description: This database contains the variables used to analyse the predictors of time to recovery and non-response to treatment of SAM in Burkina Faso. These results are have been submitted to review in Plos One in November 2020.
DOI: 10.21227/1dg2-3q53
title: Team15_5B_2020_SoCSE_KLETech

description: Datasets provided: 1. Consumption.csv:Half hourly consumption in "kwh" of "" meters for a year is given.Null values %: 51.6%2. Weather-avg/min/max.csv:Per day average/minimum/maximum temperature of meter for a year is given.3. AddInfo.csv:Some additional information for some households is provided but more than 95% values are null. The attributes "dwelling_type" and "no. of bedrooms" holds good amount of data with 24% and 13% null values respectively. Data Preprocessing:Firstly, We assigned a number to each meter id and dropped meter ids from the dataframe so that none of our columns remain of object dtype.Treating null values:We had a huge amount of null values in our dataset. Prediction in the presence of null values is highly inappropriate hence, treating null values is must.On observing the dataset we found out that for huge number of meter ids data from January to September was missing. Predicting values for those month can be a challenge.We dropped columns with more than 95% null values then transposed the dataset (Time frame now appear as row names and meters are columns). Later we converted the time into date-time format and performed data smoothening by treating outliers using interquartile range.Then we computed mean values of consumption by each meter in each month and performed interpolation on weather data to treat null values.Now we have an average value of consumption for each month by each meter but the months with null values for entire month are still not treated well.Therefore, for the meters facing unavailability of data for few months are compared to those holding values for those months. For example, consider meter1 and meter2 such that for meter 1, January to September data is unavailable and for meter 2 data is available for all the months. Now we compared the data for October, November and December of both the meters. If they are somewhat similar then we filled the data of meter 1 using the data of meter2 for January to September since their consuming patterns are similar. This was purely a trial and error. Modelling:Classified train and test data.About 80% of the data is train data and 20% is our test data.Then we verified whether our data is yet stationary or not using Dickey Fuller method. Since our dataset doesn't seem stationary by observing the trends so we used differencing method and stationarized the data.At the end we applied ARIMA on our dataset for predicting monthly consumption of each meter.
DOI: 10.5281/zenodo.4274551
title: Acousticbrainz Genre dataset. Mirdata index.

description: This is the index used in mIrdata library for Acousticbrainz genre dataset.
DOI: 10.25646/7650
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.12029562
title: Adaptive determination of the flow accumulation threshold

description: data set for article "Adaptive determination of the flow accumulation threshold for extracting drainage networks from DEMs"
DOI: 10.6084/m9.figshare.12029562.v1
title: Adaptive determination of the flow accumulation threshold

description: data set for article "Adaptive determination of the flow accumulation threshold for extracting drainage networks from DEMs"
DOI: 10.21227/htqz-0996
title: TEAM_Ashutosh_bajaj

description: NO you check yourself
DOI: 10.5281/zenodo.4274569
title: FDM schon lange ein Thema in wissenschaftlichen Bibliotheken! Materialien zum Forschungsdatenmanagement, Stiftung Universität Hildesheim (UB Hildesheim). Universitätsbibliothek Hildesheim. Forschungsdatenmanagement in Universitätsbibliotheken.

description: Zeichnungen von Patrick Hochstenbach zumForschungsdatenmanagement der Stiftung Universität Hildesheim(UB Hildesheim) Creative Commons Public Domain Dedication (CC0 1.0 Universal) &lt;https://creativecommons.org/publicdomain/zero/1.0/&gt; (to re-use freely). © Zeichnungen: Patrick Hochstenbachhttps://hochstenbach.wordpress.com
DOI: 10.17632/94b9879wts.1
title: Data for: Political Connections, Political Cycles and Abnormal Returns: Evidence from Iran

description: This database is for Political Connections, Political Cycles and Abnormal Returns: Evidence from Iran.
DOI: 10.6084/m9.figshare.12365984
title: Matrix permeability testing in the fault-hosted Gryphon U deposit (eastern Athabasca Basin, Canada): Insights into fluid-rock interactions related to deposit formation and redistribution

description: As a demonstration of the utility of testing permeability of rocks, in this study the matrix permeability was tested across a wide fault zone in Precambrian metasedimentary rocks at the basement-hosted Gryphon uranium deposit. It is part of the Wheeler River project by Denison Mines in Athabasca Basin, Saskatchewan, Canada.The permeability tests were done on unconfined dry uncut drill core pieces (4.7 to 6.1 cm diameter), sampled from inclined exploration drill holes. A portable nitrogen gas probe permeameter device was constructed and adapted for testing spots on whole drill core pieces. The device features a stiff aluminum metal frame and can hold a variety of drill core sizes. A pressure-decay method was used, with initial gas pressure charged to about 50 psi (pounds per square inch), equivalent to approximately 0.345 MPa, charged from an external tank, and an intermediate container.793 tests had good quality results of 900 attempted tests. In the failed (rejected) tests, a small leak occurred in about 5% of the samples, and a large leak in another 5%. In basement lithologies 666 tests were successful.<br><br>
DOI: 10.13140/rg.2.2.17598.31048
title: Researcher Academy Excellence - Make data accessible
DOI: 10.13140/rg.2.2.31020.08327
title: Researcher Academy Excellence - Make data accessible
DOI: 10.13140/rg.2.2.27664.64001
title: Researcher Academy Excellence - Manage and Publish research data
DOI: 10.13140/rg.2.2.20953.75366
title: Researcher Academy Excellence - Manage and Publish research data
DOI: 10.13140/rg.2.2.10467.99361
title: Certificate from ASM- IDEA Committee to Prof.Dr Eng. Ali Sabea Hammood (How to Create a Welcoming Technical Workplace: Stories and Personal Experiences) Thursday, November 12, 2020
DOI: 10.13140/rg.2.2.23889.76643
title: Certificate from ASM- IDEA Committee to Prof.Dr Eng. Ali Sabea Hammood (How to Create a Welcoming Technical Workplace: Stories and Personal Experiences) Thursday, November 12, 2020
DOI: 10.13140/rg.2.2.17178.88006
title: Certificate from ASM- IDEA Committee to Prof.Dr Eng. Ali Sabea Hammood (How to Create a Welcoming Technical Workplace: Stories and Personal Experiences) Thursday, November 12, 2020
DOI: 10.21227/mhd6-e141
title: last submit

description: monthly prediction with lgb
DOI: 10.13140/rg.2.2.32278.37446
title: Certificate from Zwick/Roll Intelligent Testing to Prof.Dr Eng. Ali Sabea Hammood (Intermediate and High Rate Tension Testing) Thursday,12th November 2020
DOI: 10.21227/z3jc-kd14
title: attention-based late fusion for weather, meter inform., consumption

description: Base Network: Attention-based CNN-LSTM