cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10419620
title: Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM

description: Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:

Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)

Building Information Data Fields:



ID

CZ

Centroid

State_Abbr

Footprint2D

Height,Area2D

BuildingType

NumFloors

Area

Standard

NumWalls

WWR_surfaces


Energy Simulation Data Fields:



Electricity_Facility[kBTU]

NaturalGas_Facility[kBTU]

Heating_Electricity[kBTU]

Cooling_Electricity[kBTU]

Heating_NaturalGas[kBTU]

Heating_Total[kBTU]

WaterSystems_Electricity[kBTU]

Lighting_Electricity[kBTU]

Equipment_Electricity[kBTU]

Fans_Electricity[kBTU]

Pumps_Electricity[kBTU]

HeatRejection_Electricity[kBTU]

HeatRecovery_Electricity[kBTU]

Surface_Outside_Face_Heat_Emission[GJ]

Zone_Exfiltration_Heat_Loss[GJ]

Zone_Exhaust_Air_Heat_Loss[GJ]

Heat_Rejection_Energy[GJ]

Anthropogenic_Emissions[GJ]


This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).
DOI: 10.5519/qd.etsxwwid
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (49 records)
DOI: 10.5281/zenodo.10636707
title: Datasets from the "TSO-DSO Interface Flow Pricing: A bilevel study on efficiency and cost allocation" article

description: Datasets used in the paper "TSO-DSO Interface Flow Pricing: A bilevel study on efficiency and cost allocation". Both the "Stylised Case Study" and the "102-Bus Case Study". The latter is presented in two scenarios: one with one congestion at the transmission network and another without congestions for the TSO.
DOI: 10.5281/zenodo.10637167
title: Datasets from the "TSO-DSO Interface Flow Pricing: A bilevel study on efficiency and cost allocation" article

description: Datasets used in the paper "TSO-DSO Interface Flow Pricing: A bilevel study on efficiency and cost allocation". Both the "Stylised Case Study" and the "102-Bus Case Study". The latter is presented in two scenarios: one with one congestion at the transmission network and another without congestions for the TSO.
DOI: 10.5522/04/25382509
title: Optical ultrasound image correction with fibre-optic shape sensors - data

description: <b>Significance:</b> Optical ultrasound (OpUS) imaging, in which ultrasound is generated and received optically, is well suited to guiding minimally procedures. 2D images can be synthesized by translation of an OpUS probe within the body. If the probe positions are not known precisely, spatial distortions in the images may occur. Fibre-optic shape sensors offer the possibility to reduce distortions by providing real-time probe positions in an all-optical, miniaturized device.<b>Aim:</b> We aimed to correct 2D OpUS images acquired with a forward-looking OpUS probe during scanning at non-uniform speeds, using tip position data from a fibre-optic shape sensor.<b>Approach:</b> An OpUS probe was combined with a fibre-optic shape sensor to allow for real-time acquisition of both ultrasound depth scans and probe tip positions, as the probe was scanned at varying speed across a wire grille imaging phantom. The resulting distorted OpUS images were corrected using the shape sensor position data.<b>Results:</b> The corrected images corresponded well to the true positions of the wires in the phantom, and distortions in the raw images caused by pauses and varying speeds of the probe movement were largely removed.<b>Conclusions:</b> Fibre-optic shape sensing is a promising modality for reducing distortions during 2D OpUS images acquired by translating single-element optical transducers.
DOI: 10.5522/04/25382509.v1
title: Optical ultrasound image correction with fibre-optic shape sensors - data

description: <b>Significance:</b> Optical ultrasound (OpUS) imaging, in which ultrasound is generated and received optically, is well suited to guiding minimally procedures. 2D images can be synthesized by translation of an OpUS probe within the body. If the probe positions are not known precisely, spatial distortions in the images may occur. Fibre-optic shape sensors offer the possibility to reduce distortions by providing real-time probe positions in an all-optical, miniaturized device.<b>Aim:</b> We aimed to correct 2D OpUS images acquired with a forward-looking OpUS probe during scanning at non-uniform speeds, using tip position data from a fibre-optic shape sensor.<b>Approach:</b> An OpUS probe was combined with a fibre-optic shape sensor to allow for real-time acquisition of both ultrasound depth scans and probe tip positions, as the probe was scanned at varying speed across a wire grille imaging phantom. The resulting distorted OpUS images were corrected using the shape sensor position data.<b>Results:</b> The corrected images corresponded well to the true positions of the wires in the phantom, and distortions in the raw images caused by pauses and varying speeds of the probe movement were largely removed.<b>Conclusions:</b> Fibre-optic shape sensing is a promising modality for reducing distortions during 2D OpUS images acquired by translating single-element optical transducers.
DOI: 10.6084/m9.figshare.25383205
title: Raw Data - StaySafe Philippines

description: A raw data of a paper entitled "<b>Factors Affecting the Intention to Use COVID-19 Contact Tracing Application “StaySafe PH”: Integrating Protection Motivation Theory, UTAUT2, and System Usability Theory</b>"
DOI: 10.5281/zenodo.10790708
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. 

This data repository is linked to https://zenodo.org/records/10523833
DOI: 10.7910/dvn/jwhb8f
title: Replication Data for-The Textual-Visual Contradictions of Light Food Advertising on China’s Taobao Platform Evidence from Co-occurrence Matrix"

description: The data used in present study focuses on the advertisements of top 10 selling light food products, a food type which is touted as health and body shape keeper, on Taobao platform in China. It aims to uncover how do these advertisements influence people’s cognition of health and beauty through text and image interaction. And 633 images which are combined into 50 longer images and 45,817 words are analyzed.
DOI: 10.5281/zenodo.10638136
title: Bacterial Grain Rot Dataset

description: Burkholderia glumae bacteria causes bacterial grain rot in rice, a disease that poses significant threats to crop yield. The distinctive feature of this disease manifests on the grains of the rice plant prior to harvest. This poses several challenges in detecting and categorizing the disease on the rice panicles. Firstly, diseased rice grains may be interspersed with healthy ones. Secondly, the rice grains on the panicles exhibit varying sizes (from small to large when detected using object detection methods). Subsequently, disease classification can be conducted based on the extent of damage to the rice panicles to determine the level of yield loss. Lastly, the challenge lies in detecting, classifying, and preprocessing for disease detection and classification using various approaches in machine learning and deep learning to develop the best method to support smart agriculture.
DOI: 10.5281/zenodo.10804733
title: Bacterial Grain Rot Dataset

description: Burkholderia glumae bacteria causes bacterial grain rot in rice, a disease that poses significant threats to crop yield. The distinctive feature of this disease manifests on the grains of the rice plant prior to harvest. This poses several challenges in detecting and categorizing the disease on the rice panicles. Firstly, diseased rice grains may be interspersed with healthy ones. Secondly, the rice grains on the panicles exhibit varying sizes (from small to large when detected using object detection methods). Subsequently, disease classification can be conducted based on the extent of damage to the rice panicles to determine the level of yield loss. Lastly, the challenge lies in detecting, classifying, and preprocessing for disease detection and classification using various approaches in machine learning and deep learning to develop the best method to support smart agriculture.
DOI: 10.5281/zenodo.10638135
title: Bacterial Grain Rot Dataset

description: Burkholderia glumae bacteria causes bacterial grain rot in rice, a disease that poses significant threats to crop yield. The distinctive feature of this disease manifests on the grains of the rice plant prior to harvest. This poses several challenges in detecting and categorizing the disease on the rice panicles. Firstly, diseased rice grains may be interspersed with healthy ones. Secondly, the rice grains on the panicles exhibit varying sizes (from small to large when detected using object detection methods). Subsequently, disease classification can be conducted based on the extent of damage to the rice panicles to determine the level of yield loss. Lastly, the challenge lies in detecting, classifying, and preprocessing for disease detection and classification using various approaches in machine learning and deep learning to develop the best method to support smart agriculture.
DOI: 10.5281/zenodo.10805462
title: Bacterial Grain Rot Dataset

description: Burkholderia glumae bacteria causes bacterial grain rot in rice, a disease that poses significant threats to crop yield. The distinctive feature of this disease manifests on the grains of the rice plant prior to harvest. This poses several challenges in detecting and categorizing the disease on the rice panicles. Firstly, diseased rice grains may be interspersed with healthy ones. Secondly, the rice grains on the panicles exhibit varying sizes (from small to large when detected using object detection methods). Subsequently, disease classification can be conducted based on the extent of damage to the rice panicles to determine the level of yield loss. Lastly, the challenge lies in detecting, classifying, and preprocessing for disease detection and classification using various approaches in machine learning and deep learning to develop the best method to support smart agriculture.
DOI: 10.5281/zenodo.10781652
title: ESI-CorA: SARS-CoV-2-Abwassersurveillance

description: Das Vorhaben "Emergency Support Instrument - Nachweis von SARS-CoV-2 im Abwasser" (ESI-CorA) lief von November 2021 bis März 2023. Zwanzig Kläranlagenstandorte wurden in Deutschland ausgewählt, die im Februar 2022 gestaffelt mit der Überwachung von SARS-CoV-2 im Abwasser begonnen haben. Das zentrale Ziel in ESI-CorA war die Vorbereitung und Durchführung der bundesweiten Pilotphase zur Überwachung von SARS-CoV-2 und seiner Varianten im Abwasser. Unter anderem wurde das Verfahren zur Normalisierung der Rohdaten, der angewandten PCR-Analytik und der Berechnung der Trenddynamiken untersucht.
Weitere Informationen sind im ESI-CorA Projektblatt des Karlsruher Instituts für Technologie (KIT) zu finden.
DOI: 10.5281/zenodo.10781653
title: ESI-CorA: SARS-CoV-2-Abwassersurveillance

description: Das Vorhaben "Emergency Support Instrument - Nachweis von SARS-CoV-2 im Abwasser" (ESI-CorA) lief von November 2021 bis März 2023. Zwanzig Kläranlagenstandorte wurden in Deutschland ausgewählt, die im Februar 2022 gestaffelt mit der Überwachung von SARS-CoV-2 im Abwasser begonnen haben. Das zentrale Ziel in ESI-CorA war die Vorbereitung und Durchführung der bundesweiten Pilotphase zur Überwachung von SARS-CoV-2 und seiner Varianten im Abwasser. Unter anderem wurde das Verfahren zur Normalisierung der Rohdaten, der angewandten PCR-Analytik und der Berechnung der Trenddynamiken untersucht.
Weitere Informationen sind im ESI-CorA Projektblatt des Karlsruher Instituts für Technologie (KIT) zu finden.
DOI: 10.3886/e114016
title: Replication data for: Social Connectedness: Measurement, Determinants, and Effects

description: Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook's scale as well as the relative representativeness of Facebook's user body, these data provide the first comprehensive measure of friendship networks at a national level.
DOI: 10.3886/e114016v1-146902
title: Replication data for: Social Connectedness: Measurement, Determinants, and Effects

description: Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body 
of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an 
inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign
 country. Given Facebook's scale as well as the relative representativeness of Facebook's user body, these data provide the first comprehensive measure of friendship networks at a national level.
DOI: 10.6084/m9.figshare.25383100
title: <b>New species of </b><b><i>Hohenbuehelia</i></b><b> (Pleurotaceae, Agaricales) from Northwestern Siberia, Russia</b>

description: During the surveying of fleshy fungi of the Khanty-Mansi Autonomous Okrug–Yugra region (Northwestern Siberia, Russia), we found a distinct new species of <i>Hohenbuehelia</i> (Pleurotaceae, Agaricales). This paper is devoted to the morphological and ecological description of the new <i>Hohenbuehelia</i> and its phylogenetic position among the currently described species of this genus with publicly available ITS sequences. The new species differs reliably from other known species of the genus by the combination of strongly developed tomentum, dark lamellae, copiously encrusted metuloids and abundant crystals embedded in the pileipellis<br>
DOI: 10.6084/m9.figshare.25383226
title: Spatial models from "Spatial multi-omic map of human myocardial infarction"

description: Sample-level spatial models of cell colocalization, and functional dependencies as generated by mistyR for the paper:<br>Kuppe, C., Ramirez Flores, R.O., Li, Z. <i>et al.</i> Spatial multi-omic map of human myocardial infarction. <i>Nature</i> <b>608</b>, 766–777 (2022). https://doi.org/10.1038/s41586-022-05060-x
DOI: 10.6084/m9.figshare.25383226.v1
title: Spatial models from "Spatial multi-omic map of human myocardial infarction"

description: Sample-level spatial models of cell colocalization, and functional dependencies as generated by mistyR for the paper:<br>Kuppe, C., Ramirez Flores, R.O., Li, Z. <i>et al.</i> Spatial multi-omic map of human myocardial infarction. <i>Nature</i> <b>608</b>, 766–777 (2022). https://doi.org/10.1038/s41586-022-05060-x
DOI: 10.5281/zenodo.10688119
title: Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials

description: This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP).  The computational details are given in the manuscript.
DOI: 10.5281/zenodo.10805583
title: QARAQALPAQ XALÍQ ERTEKLERINDEGI AYÍRÍM ADAM ATLARÍ HAQQÍNDA
DOI: 10.5281/zenodo.10805584
title: QARAQALPAQ XALÍQ ERTEKLERINDEGI AYÍRÍM ADAM ATLARÍ HAQQÍNDA
DOI: 10.6084/m9.figshare.25383235
title: <b>Dataset that will be used in the proyect</b>

description: This study aim to predict the annual herd milk yield, lactation, and reproductive cycle stages in a high-input dairy herd in a zone with prolonged thermal stress. Also, the impact of climatic conditions on milk yield and productive and reproductive status will be assessed.
DOI: 10.6084/m9.figshare.25383235.v1
title: <b>Dataset that will be used in the proyect</b>

description: This study aim to predict the annual herd milk yield, lactation, and reproductive cycle stages in a high-input dairy herd in a zone with prolonged thermal stress. Also, the impact of climatic conditions on milk yield and productive and reproductive status will be assessed.
DOI: 10.18112/openneuro.ds004998.v1.0.1
title: Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.
DOI: 10.5281/zenodo.10211008
title: Data for: Atomistic description of Si etching with HCl

description: Dataset with all the CONTCAR files of the minima and TS of the DFT simulations. The computational details are given in the manuscript.
DOI: 10.5281/zenodo.10211009
title: Data for: Atomistic description of Si etching with HCl

description: Dataset with all the CONTCAR files of the minima and TS of the DFT simulations. The computational details are given in the manuscript.
DOI: 10.21227/4zht-tr07
title: ICS-ADD -A Smart Industry Testbed Dataset for Cyber-Physical Security Monitoring Testing

description: The increasing integration of cyber-physical systems in industrial environments has under scored the critical need for robust security measures to counteract evolving cyber threats. In response to this need, this work introduces an open-source dataset designed to enhance the development and evaluation of cybersecurity solutions for smart industries. The dataset comprises a traffic capture of an industrial control system (ICS) subjected to a variety of simulated cyber-attacks, including but not limited to denial of service (DoS), man-in-the-middle (MITM), and malware infiltration. In addition to raw network traffic, the dataset presents the output of two widely utilized open-source security monitoring tools, OSSIM (Open Source Security Information Management) and Suricata, which offer insights into the detection and analysis capabilities of existing security frameworks against these simulated threats. By providing a detailed account of attack methodologies, network traffic characteristics, and tool responses, this dataset serves as a valuable resource for researchers and practitioners aiming to develop, test, and benchmark new cyber-physical security monitoring and detection technologies.
DOI: 10.57710/alba-es-2023097906
title: Unraveling the electronic interaction of perovskite single crystal with the C60 charge transporting layer

description: In this research proposal, our primary objective is to investigate the electronic properties of triple cation (TC)-based single crystal perovskite through the use of Angle-Resolved Photoemission Spectroscopy (ARPES). Additionally, we aim to delve into the electronic interaction dynamics between the perovskite and C60. This comprehensive study will serve as a model inquiry, providing valuable insights into the intricate electronic interplay between perovskite materials and charge transporting layers, shedding light on their fundamental nature and behavior.
DOI: 10.25345/c5xp6vf1q
title: MassIVE MSV000094290 - Fervidibacter sacchari diferential proteomics under different growth conditions
title: Fervidibacter sacchari PD1 cells were grown with beta-glucan, gellan gum, locust bean gum, starch, or xyloglucan as sole carbon/energy sources. Cellular and secreted proteins under each condition were analyzed with DIA proteomics.