cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16742911
title: random_sample_1

description: Experimental averages, management, and environmental data for every experiment, across all genotypes, including sites with missing lat/longs:<br>Random-sample imputed data for each site, including linearly interpolated satellite data, environment and management variables. Each is the first of 25 large imputations. Contact me for the entire (bulky) dataset.
DOI: 10.6084/m9.figshare.16742515
title: random_sample_rescaled_1

description: Experimental averages, management, and environmental data for every experiment, across all genotypes, including sites with missing lat/longs:<br>Random-sample imputed data for each site, rescaled to zero mean and unit variance, including linearly interpolated satellite data, environment and management variables. Each is the first of 25 large imputations. Contact me for the entire (bulky) dataset.
DOI: 10.6084/m9.figshare.16743745.v1
title: Chickpea

description: A flat csv file contaning all data for the labeled species, at the resolution of site-variety-year combinations. Wheat is split into three files as there are too many records. Barley is excluded for ethical reasons, available on request.
DOI: 10.6084/m9.figshare.16742407
title: RF_imputed_1

description: Experimental averages, management, and environmental data for every experiment, across all genotypes, including sites with missing lat/longs:<br>Random-forest imputed data for each site, including linearly interpolated satellite data, environment and management variables. Each is the first of 25 large imputations. Contact me for the entire (bulky) dataset.<br><br>
DOI: 10.6084/m9.figshare.16742479
title: RF_imputed_rescaled1

description: Experimental averages, management, and environmental data for every experiment, across all genotypes, including sites with missing lat/longs:<br>Random-forest imputed data for each site, rescaled to zero mean and unit variance, including linearly interpolated satellite data, environment and management variables. Each is the first of 25 large imputations. Contact me for the entire (bulky) dataset.
DOI: 10.25678/00042k
title: Data for: Performance and Dynamics of Active Greywater Heat Recovery in Buildings

description: This package provides material related to the paper: `B. Hadengue, E. Morgenroth, T. A. Larsen, L. Baldini (2022) Performance and Dynamics of Active Greywater Heat Recovery in Buildings, Applied Energy, Volume 305, https://doi.org/10.1016/j.apenergy.2021.117677`## Paper AbstractIn the effort to de-carbonize the building stock, heat pumps are increasingly utilized in Switzerland, with 70% of the fast-growing heat pump market using ambient air as heat source. Inexpensive and easy to implement, these heat pumps are, however, less efficient than their ground- or water-source counterparts. In this modeling study, we aim at increasing the efficiency of air-source heat pumps using domestic greywater-contained heat. We assess the performance improvement relative to standard heat pump configurations across various climates, seasons, building envelopes, and domestic hot water consumption patterns. The results show that the annually-averaged COP improves by 4.1% on average – ranging from 0.6% to 7.5%. This efficiency gain translates on average to 1.8 kWh/week of compressor electricity savings. Although attractive due to its simplicity, the proposed open-loop configuration – preheating of an external heat source – only leads to moderate performance improvement of air-source heat pumps. Based on these results, we extensively discuss and compare alternative system configurations and identify several fundamental differences in the heat recovery dynamics of each configuration. We show that closed-loop systems – using greywater as direct heat source – show the largest performance improvement potential, although being more expensive and complex to implement.
DOI: 10.18112/openneuro.ds003643.v1.0.4
title: Le Petit Prince: A multilingual fMRI corpus using ecological stimuli
DOI: 10.6084/m9.figshare.16610437
title: mod_frame1_rescaled.rds

description: Data frame for code in Nat. Plants.<br>
DOI: 10.6084/m9.figshare.16610440
title: variety_frame.rds

description: An R data file containing all site-variety combinations across all species and years.<br><br>Load into an R workspace with a *lot* of spare RAM.<br>
DOI: 10.6084/m9.figshare.16610446
title: mod_frame1.rds

description: RDS file used for supplementary data in Nat Plants (loads full site-variety-year database with some QC)<br>
DOI: 10.6084/m9.figshare.16610449
title: site_frame.csv

description: Full dataset of site-aggegreated phenotypic, environmental and management data. Site-years only, not aggrgeated by genotype.<br>
DOI: 10.6084/m9.figshare.16610443
title: site_frame.csv.zip

description: A flat csv including site-averaged phenotypes and unique experiment identifier (BOMDom_Trial_ID) that can used to like these data to imputed environmental data.<br><br>
DOI: 10.6084/m9.figshare.11604201.v1
title: Quantitation of human and murine Fc gamma receptors

description: Here, data sets on human or murine Fc gamma receptor expression in steady state are provided . <br>They refer to the manuscript <i>"There is (Scientific) Strength in Numbers: A Comprehensive Quantitation of Fc Gamma Receptor Numbers on Human and Murine Peripheral Blood Leukocytes"</i> by Kerntke, Nimmerjahn and Biburger. <br>(Frontiers in Immunology)<br>
DOI: 10.6084/m9.figshare.15044151
title: Extended Data Set: Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol

description: Extended dataFigshare: Extended Data Set: Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol. pdf, This systematic review contains following extended data:· Appendix 1: PubMed Search Strategy· Appendix 2: JBI _Critical Appraisal_Quality Assessment Checklists· Appendix 3: Data extraction tool Reporting guidelinesFigshare: PRISMA-P checklist for ‘Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol’<br>
DOI: 10.6084/m9.figshare.15044151.v1
title: Extended Data Set: Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol

description: Extended dataFigshare: Extended Data Set: Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol. pdf, This systematic review contains following extended data:· Appendix 1: PubMed Search Strategy· Appendix 2: JBI _Critical Appraisal_Quality Assessment Checklists· Appendix 3: Data extraction tool Reporting guidelinesFigshare: PRISMA-P checklist for ‘Socio-cognitive determinants affecting insulin adherence/non-adherence in late adolescents and young adults with type 1 diabetes: a systematic review protocol’<br>
DOI: 10.15151/esrf-es-514141819
title: Catalytic removal of nitrogen monoxide, carbon monoxide and hydrocarbons

description: The study is a continuation of our investigations on understanding mechanistic details of three-way catalysts by exploring the L edges of the noble metal components (previous proposal MA-4916 on Pd L edges). Hence, the aim of the present proposal it to conduct operando HERFD-XANES studies at Rh L edges for a Rh/CeO2-ZrO2 three-way catalyst for elucidating: (1) the interaction of CO, C3H6 and NO with the noble metal under reaction conditions, and (2) performing spatially resolved measurements along the catalyst bed at selected temperatures during light-off /light-out.
DOI: 10.6084/m9.figshare.11604201
title: Quantitation of human and murine Fc gamma receptors

description: Here, data sets on human or murine Fc gamma receptor expression in steady state are provided . <br>They refer to the manuscript <i>"There is (Scientific) Strength in Numbers: A Comprehensive Quantitation of Fc Gamma Receptor Numbers on Human and Murine Peripheral Blood Leukocytes"</i> by Kerntke, Nimmerjahn and Biburger. <br>(Frontiers in Immunology)<br><br>(in 10/2021 some data points for murine FcgRIIb were restored which had been lacking in version1)
DOI: 10.6084/m9.figshare.11604201.v2
title: Quantitation of human and murine Fc gamma receptors

description: Here, data sets on human or murine Fc gamma receptor expression in steady state are provided . <br>They refer to the manuscript <i>"There is (Scientific) Strength in Numbers: A Comprehensive Quantitation of Fc Gamma Receptor Numbers on Human and Murine Peripheral Blood Leukocytes"</i> by Kerntke, Nimmerjahn and Biburger. <br>(Frontiers in Immunology)<br>
DOI: 10.5281/zenodo.3743394
title: Replication Package: A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts

description: The replication package of the paper "A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts" including the dataset, results and tools
DOI: 10.5281/zenodo.3743393
title: Replication Package: A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts

description: The replication package of the paper "A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts" including the dataset, results and tools
DOI: 10.5517/ccdc.csd.cc25mpbv
title: CCDC 2014917: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc25mpcw
title: CCDC 2014918: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc25mpdx
title: CCDC 2014919: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc25mpgz
title: CCDC 2014921: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc25mph0
title: CCDC 2014922: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc25mpj1
title: CCDC 2014923: Experimental Crystal Structure Determination

description: Related Article: Kongzhao Su, Wenjing Wang, Shunfu Du, Chunqing Ji, Mi Zhou, Daqiang Yuan|2020|J.Am.Chem.Soc.|142|18060|doi:10.1021/jacs.0c07367
DOI: 10.5517/ccdc.csd.cc28tytw
title: CCDC 2110319: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, John B. Bremner|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28v1kr
title: CCDC 2110404: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Effendy, Allan H. White|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28vc3m
title: CCDC 2110700: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28vc4n
title: CCDC 2110701: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28vj9z
title: CCDC 2110861: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Effendy, Allan H. White|2021|CSD Communication|||
DOI: 10.6084/m9.figshare.16743856
title: Wheat_part3

description: A flat csv file contaning all data for the labeled species, at the resolution of site-variety-year combinations. Wheat is split into three files as there are too many records. Barley is excluded for ethical reasons, available on request. Part 3/3 of the wheat data.
DOI: 10.6084/m9.figshare.16743790
title: Oat

description: A flat csv file contaning all data for the labeled species, at the resolution of site-variety-year combinations. Wheat is split into three files as there are too many records. Barley is excluded for ethical reasons, available on request.
DOI: 10.5281/zenodo.3956067
title: pH reactor dataset

description: Dataset of noisy input and output measurements collected from a simulator of a pH reactor. The system is single input single output. The dataset consists of the following files: 'PH_U_Train.csv' and 'PH_Y_Train.csv': training dataset. The training dataset consists of 15 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment. 'PH_U_Val.csv' and 'PH_Y_Val.csv': validation dataset. The validation dataset consists of 4 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment. 'PH_U_Test.csv' and 'PH_Y_Test.csv': test dataset. The test dataset consists of 1 experiment of 2000 (u, y) samples. 'PH_U.csv' and 'PH_Y.csv': concatenation of the training, validation, and test datasets. Moreover, the parameters of an Incrementally Input-to-State Stable LSTM neural network trained to learn the dynamical system are reported in the file 'LSTM_parameters.mat'. If you use this data please cite the related paper Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). Learning model predictive control with long short‐term memory networks. <em>International Journal of Robust and Nonlinear Control.</em>
description: {"references": ["R. C. Hall, and D. E. Seborg, \"Modelling and self-tuning control of a multivariable pH neutralization process part I: Modelling and multiloop control.\" 1989 American Control Conference. IEEE, 1989.", "F. Bonassi, E. Terzi, M. Farina, and R. Scattolini, \"LSTM neural networks: Input to state stability and probabilistic safety verification\", arXiv preprint arXiv:1912.04377, 2019", "Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). \"Learning model predictive control with long short\u2010term memory networks\".\u00a0International Journal of Robust and Nonlinear Control"]}
DOI: 10.5281/zenodo.3956066
title: pH reactor dataset

description: Dataset of noisy input and output measurements collected from a simulator of a pH reactor. The system is single input single output. The dataset consists of the following files: 'PH_U_Train.csv' and 'PH_Y_Train.csv': training dataset. The training dataset consists of 15 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment. 'PH_U_Val.csv' and 'PH_Y_Val.csv': validation dataset. The validation dataset consists of 4 experiments, each one with 2000 (u, y) samples. The i-th column corresponds to the input (or output) variable of the i-th experiment. 'PH_U_Test.csv' and 'PH_Y_Test.csv': test dataset. The test dataset consists of 1 experiment of 2000 (u, y) samples. 'PH_U.csv' and 'PH_Y.csv': concatenation of the training, validation, and test datasets. Moreover, the parameters of an Incrementally Input-to-State Stable LSTM neural network trained to learn the dynamical system are reported in the file 'LSTM_parameters.mat'. If you use this data please cite the related paper Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). Learning model predictive control with long short‐term memory networks. <em>International Journal of Robust and Nonlinear Control.</em>
description: {"references": ["R. C. Hall, and D. E. Seborg, \"Modelling and self-tuning control of a multivariable pH neutralization process part I: Modelling and multiloop control.\" 1989 American Control Conference. IEEE, 1989.", "F. Bonassi, E. Terzi, M. Farina, and R. Scattolini, \"LSTM neural networks: Input to state stability and probabilistic safety verification\", arXiv preprint arXiv:1912.04377, 2019", "Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021). \"Learning model predictive control with long short\u2010term memory networks\".\u00a0International Journal of Robust and Nonlinear Control"]}
DOI: 10.5281/zenodo.5550315
title: Dataset of paper: Deep Learning Models for Automated Identification of Scheduling Policies

description: The training set and testing set for the paper: Deep Learning Models for Automated Identification of Scheduling Policies
DOI: 10.5281/zenodo.5550314
title: Dataset of paper: Deep Learning Models for Automated Identification of Scheduling Policies

description: The training set and testing set for the paper: Deep Learning Models for Automated Identification of Scheduling Policies
DOI: 10.6073/pasta/9a201937e8f4d294ad7b6da3c922f882
title: Luquillo Critical Zone Observatory (LCZO) Data repository on HydroShare

description: Data archive for the Luquillo Critical Zone Observatory (LCZO), Puerto Rico. Active from 2009 to 2020. LCZO focuses on how Critical Zone processes and water balances differ in tropical landscapes with contrasting bedrock but similar climatic and environmental histories. Our infrastructure, sampling strategy, and data management system include watersheds underlain by granodiorite (GD) and volcaniclastic (VC) bedrock in the natural laboratory of the Luquillo Mountains, Puerto Rico. LCZO is one of ten NSF-supported critical zone observatories. The archive is here: https://www.hydroshare.org/group/144
DOI: 10.5281/zenodo.3733253
title: Power profiles of loads, DERs and MGs' internal units

description: Dataset of power profiles used in the Simulation section of the paper Bonassi, F., La Bella, A., Lazzari, R., Sandroni, C., & Scattolini, R. (2021). Supervised control of hybrid AC-DC grids for power balance restoration. <em>Electric Power Systems Research</em>, <em>196</em>, 107107. <br> Please cite the paper if you use this data. <strong>The dataset is made of the following files:</strong> 'cluster_references.xls': Table of the reference power programs of the four clusters. Column 'Cluster_i_ref': Power program of the i-th cluster [kW]<br> 'converter_schedules.xls': Table of power converters' scheduled power profiles. Column 'Converter_i_P': Active power program of the i-th power converter [kW] Column 'Converter_i_Q': Reactive power program of the i-th power converter [kVAR]<br> 'load_actual.xls': Table of the actual power profiles of the loads connected to the grid. Column 'Load_i_P': Active power absorbed by the i-th load [kW] Column 'Load_i_Q': Reactive power absorbed by the i-th load [kVAR]<br> 'load_forecasts.xls': Table of the power profiles' forecasts of the loads connected to the grid. Column 'Load_i_P': Active power forecast of the i-th load [kW] Column 'Load_i_Q': Reactive power forecast of the i-th load [kVAR]<br> 'MG_schedules.xls': Table of MGs' schedules for dispatchable units. Column 'MG_i_Gen_P': Active power schedule for the dispatchable generator of the i-th MG [kW] Column 'MG_i_Gen_Q': Reactive power schedule for the dispatchable generator of the i-th MG [kVAR] Column 'MG_i_Batt_P': Active power schedule for the battery of MG_i [kW] Column 'MG_i_Batt_Q': Reactive power schedule for the battery of MG_i [kVAR]<br> 'pv_actual.xls': Table of the actual power profiles of the PV panels. Column 'MG_i_PV_P': Active power injected by the PV panel of MG_i [kW] <br> 'pv_forecasts.xls': Table of the power profiles' forecasts of the PV panels. Column 'MG_i_PV_P': Active power forecast of the PV panel of MG_i [kW]<br> 'units_network_data.xls': Table of units' and network data. Sheet 'AC nodes': Data concerning the AC buses. Sheet 'AC branches': Data concerning the AC branches connecting the AC nodes. Sheet 'AC-DC interfaces': Data of the AC-DC interfacing power converters. Sheet 'DC links': Data concerning the DC links. Sheet 'MGs Generators': Nominal data of MGs' internal generators. Sheet 'MGs Batteries': Nominal data of MGs' internal batteries.
description: {"references": ["Bonassi, F., La Bella, A., Lazzari, R., Sandroni, C., & Scattolini, R. (2021). Supervised control of hybrid AC-DC grids for power balance restoration. Electric Power Systems Research, 196, 107107."]}
DOI: 10.6073/pasta/8ae4e2e09daa248f4c44646063ab287e
title: Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico

description: The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes &gt;|1| m in either period. Canopy layers &lt;10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files.
DOI: 10.2905/5ef706bb-cced-47aa-b5f3-f56112b420c2
title: Sentinel 2 L1C cloud-free annual composites

description: Sentinel 2 L1C cloud-free composites over the tropical belt have been produced based on all Sentinel 2A and 2B imagery from October 2015 to October 2017 and entire 2018, 2019, 2020 (January-December)
DOI: 10.6073/pasta/fbf93bd783e830de9e0c69af383b3162
title: LEF Sabana weather station from 2018

description: The data archive is here: https://doi.org/10.2737/RDS-2021-0030 This data publication contains 1 hour values for field data from the weather station at Sabana Field Station, on top of a scaffold tower in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2018 through 2020. The weather station collects solar radiation, photosynthetically active radiation (PAR), air temperature, air relative humidity, precipitation, barometric pressure, and wind (speed and direction broken down to octants with a wind rose). The weather data have been recorded at the Sabana Field station since 2001; this is the third location of the weather station. The weather station was located on the laboratory roof from 2001-2007, and a demonstrative weather station existed 2007-2018. This newer weather station’s data should not be directly compared to the previous weather stations, as it is in a different location.
DOI: 10.5281/zenodo.5550340
title: US-SB2 2016-2020 ReddyProc Output

description: Post processed data from Ameriflux site US-SB2 (Switchgrass).
DOI: 10.5281/zenodo.5550339
title: US-SB2 2016-2020 ReddyProc Output

description: Post processed data from Ameriflux site US-SB2 (Switchgrass).