cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25226064.v1
title: Modeling Recurrent Failures on Large Directed Networks

description: Many lifeline infrastructure systems consist of thousands of components configured in a complex directed network. Disruption of the infrastructure constitutes a recurrent failure process over a directed network. Statistical inference for such network recurrence data is challenging because of the large number of nodes with irregular connections among them. Motivated by 16 years of Scottish Water operation records, we propose a network Gamma-Poisson Autoregressive NHPP (GPAN) model for recurrent failure data from large-scale directed physical networks. The model consists of two layers: the temporal layer applies a Non-Homogeneous Poisson Process (NHPP) with node-specific frailties, and the spatial layer uses a well-orchestrated gamma-Poisson autoregressive scheme to establish correlations among the frailties. Under the network-GPAN model, we develop a sum-product algorithm to compute the marginal distribution for each frailty conditional on the recurrence data. The marginal conditional frailty distributions are useful for predicting future failures based on historical data. In addition, the ability to rapidly compute these marginal distributions allows adoption of an EM type algorithm for estimation. Through a Bethe approximation, the output from the sum-product algorithm is used to compute maximum log-likelihood estimates. Applying the methods to the Scottish Water network, we demonstrate utility in aiding operation management and risk assessment of the water utility. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.ewte4u
title: Occurrence Download

description: A dataset containing 480 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bactrocera latifrons (Hendel, 1912)"
 ]
}

 The dataset includes 480 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076844-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25134317
title: Federated Offline Reinforcement Learning

description: Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this article, we propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.25134317.v2
title: Federated Offline Reinforcement Learning

description: Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this article, we propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.ubq7x7
title: Occurrence Download

description: A dataset containing 10663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, Ukraine, Germany, Poland, Switzerland, Austria, Czechia, Slovakia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Brachythecium rutabulum (Hedw.) Schimp."
 ]
}

 The dataset includes 10663 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076845-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25134317.v1
title: Federated Offline Reinforcement Learning

description: Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this paper, we propose a multi-site Markov decision process model which allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings.
DOI: 10.5281/zenodo.7277685
title: Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage

description: The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under projects  UIDB/00760/2020,  and CEECIND/01423/2021. Cátia Silva is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with Ph.D. grant reference SFRH/BD/144200/2019.
DOI: 10.5281/zenodo.7277686
title: Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage

description: The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under projects  UIDB/00760/2020,  and CEECIND/01423/2021. Cátia Silva is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with Ph.D. grant reference SFRH/BD/144200/2019.
DOI: 10.15468/dl.fzc9c6
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plumbago amplexicaulis Oliv."
 ]
}

 The dataset includes 42 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076846-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zvpshs
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinanga humilis A.J.Hend., N.K.Ban & N.Q.Dung"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076849-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3nmpj9
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polygala latistyla Pendry"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076847-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.abjhha
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platanthera anatina Ormerod"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076850-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.32pe78
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phymaspermum parvifolium (DC.) Benth. & Hook.fil."
 ]
}

 The dataset includes 39 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076851-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.txwsmz
title: Occurrence Download

description: A dataset containing 251 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phreatia inversa Schltr."
 ]
}

 The dataset includes 251 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076848-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.6783288
title: Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers

description: The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.

The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/

The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
DOI: 10.5281/zenodo.6783289
title: Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers

description: The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.

The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/

The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
DOI: 10.5281/zenodo.6778781
title: A full-year data regarding a smart building

description: The smart building represented by the dataset is divided into five zones, where each zone has 2 to 3 offices. The building has photovoltaic panels with a generation peak of 7.5 kW, inside sensors, light intensity control, and all consumption measured by load type. The data was collected in 5 minutes periods.

By zones, the buildings have a distribution of 6 researchers in Zone#1, 5 researchers in Zone#2, 5 researchers in Zone#3, 3 researchers in Zone#4, and 5 researchers in Zone#5. Zone#1 includes a meeting room, and Zone#2 includes a server room. Regarding the server room, its HVAC unit was measured in HVAC#2, however, near the end of the year, the unit was removed from the monitoring system.

The light intensity values represent the current state of the lamps and have a linear correlation with the lamp’s consumption.

The dataset represents raw data without any treatment, this means that it is possible to find errors. The data do not have missing reading periods, but they can have a fixed zero (0) value, indicating a failure in the system. You can assume that periods with zero voltage represent an error in that zone at that period.

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
DOI: 10.5281/zenodo.6778782
title: A full-year data regarding a smart building

description: The smart building represented by the dataset is divided into five zones, where each zone has 2 to 3 offices. The building has photovoltaic panels with a generation peak of 7.5 kW, inside sensors, light intensity control, and all consumption measured by load type. The data was collected in 5 minutes periods.

By zones, the buildings have a distribution of 6 researchers in Zone#1, 5 researchers in Zone#2, 5 researchers in Zone#3, 3 researchers in Zone#4, and 5 researchers in Zone#5. Zone#1 includes a meeting room, and Zone#2 includes a server room. Regarding the server room, its HVAC unit was measured in HVAC#2, however, near the end of the year, the unit was removed from the monitoring system.

The light intensity values represent the current state of the lamps and have a linear correlation with the lamp’s consumption.

The dataset represents raw data without any treatment, this means that it is possible to find errors. The data do not have missing reading periods, but they can have a fixed zero (0) value, indicating a failure in the system. You can assume that periods with zero voltage represent an error in that zone at that period.

 

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
DOI: 10.5281/zenodo.4352081
title: Single-unit and multi-unit peer-to-peer transactions in a microgrid (uGIM dataset)

description: uGIM is a microgrid intelligent management platform that can represent individual end-users using a multi-agent approach. This dataset has data regarding two weeks (May 13th to May 18th, 2019, and September 30th to October 6th, 2019) where auction-based peer-to-peer transactions were performed in a real microgrid.

The data was collected by uGIM agents and auctions were executed every hour. The hour-ahead auctions are performed by the sellers in a fully distributed approach. To create this dataset two NanoPi M1 Plus (with 1.2 GHz quad-core CPUs and 1 GB of RAM running the Ubuntu 16.04.6 LTS operating system), and three Raspberry Pi Model B+ (with 1.4 GHz 64-bit quad-core CPUs and GB of RAM running the Raspberry Pi OS operating system) were used.

uGIM related publications: - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. https://doi.org/10.1016/j.measurement.2019.107427 - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. https://doi.org/10.1109/ACCESS.2020.2985254 - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. https://doi.org/10.14201/gredos.144238 - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. https://doi.org/10.1016/j.jclepro.2019.118154

 

(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UIDB/00760/2020.
DOI: 10.5281/zenodo.4352082
title: Single-unit and multi-unit peer-to-peer transactions in a microgrid (uGIM dataset)

description: uGIM is a microgrid intelligent management platform that can represent individual end-users using a multi-agent approach. This dataset has data regarding two weeks (May 13th to May 18th, 2019, and September 30th to October 6th, 2019) where auction-based peer-to-peer transactions were performed in a real microgrid.

The data was collected by uGIM agents and auctions were executed every hour. The hour-ahead auctions are performed by the sellers in a fully distributed approach. To create this dataset two NanoPi M1 Plus (with 1.2 GHz quad-core CPUs and 1 GB of RAM running the Ubuntu 16.04.6 LTS operating system), and three Raspberry Pi Model B+ (with 1.4 GHz 64-bit quad-core CPUs and GB of RAM running the Raspberry Pi OS operating system) were used.

uGIM related publications: - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. https://doi.org/10.1016/j.measurement.2019.107427 - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. https://doi.org/10.1109/ACCESS.2020.2985254 - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. https://doi.org/10.14201/gredos.144238 - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. https://doi.org/10.1016/j.jclepro.2019.118154

 

(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.
description: This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UIDB/00760/2020.