cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3890321
title: Additional environmental extensions for EXIOBASE3 based on USEEEIO

description: This dataset links the commodity sectors of EXIOBASE3.7 (i.e., pxp version) to additional environmental extensions (e.g., formaldehyde, toluene, acrolein, etc.) based on USEEIOv1.1 extensions. Only environmental extensions that are not present in EXIOBASE were added. This satellite matrix can thus simply be added to the satellite matrix of EXIOBASE without fear of double counting emissions. 1,766 emissions were added. The dataset also includes a characterization matrix for these additional flows, characterized (for 36 impact categories) using the IMPACT World+ life cycle impact assessment method (Bulle, 2019). The methodology behind the determination of these additional environmental extensions is described in (Agez et al., 2021), and is more extensively described in (Muller, 2019), albeit in french. In short, we reapply the emission factors from USEEIO (so specific to the US) to all countries of EXIOBASE, which has obvious limitations, but can constitute a good-enough estimation in some cases. (Agez et al., 2021) and (Muller, 2019) for example, show that these additional environmental extensions have a significant effect on some impact categories, namely, ecotoxicity and ozone layer depletion. In other words, normal environmental extensions of EXIOBASE are not equipped to study these impact categories. Other categories are marginally impacted (from 0.5 to 3% increase of the impact when looking at the whole economy's impact). Version of IW+ used: 10.5281/zenodo.3521034
description: {"references": ["Agez, M., Muller, E., Patouillard, L., Arvesen, A., Margni, M., Samson, R., & Majeau-Bettez, G. (2021). Correcting remaining truncations in hybrid LCA database compilation. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.13132", "Muller, E. (2019). Estimation et compl\u00e9tion des flux \u00e9l\u00e9mentaires manquants du tableau entr\u00e9es-sorties multir\u00e9gional EXIOBASE. Polytechnique Montreal. Retrieved from https://publications.polymtl.ca/3920/", "Bulle, C., Margni, M., Patouillard, L., Boulay, A. M., Bourgault, G., De Bruille, V., \u2026 Jolliet, O. (2019). IMPACT World+: a globally regionalized life cycle impact assessment method. International Journal of Life Cycle Assessment, 1653\u20131674. https://doi.org/10.1007/s11367-019-01583-0"]}
DOI: 10.5281/zenodo.3890320
title: Additional environmental extensions for EXIOBASE3 based on USEEEIO

description: This dataset links the commodity sectors of EXIOBASE3.7 (i.e., pxp version) to additional environmental extensions (e.g., formaldehyde, toluene, acrolein, etc.) based on USEEIOv1.1 extensions. Only environmental extensions that are not present in EXIOBASE were added. This satellite matrix can thus simply be added to the satellite matrix of EXIOBASE without fear of double counting emissions. 1,766 emissions were added. The dataset also includes a characterization matrix for these additional flows, characterized (for 36 impact categories) using the IMPACT World+ life cycle impact assessment method (Bulle, 2019). The methodology behind the determination of these additional environmental extensions is described in (Agez et al., 2021), and is more extensively described in (Muller, 2019), albeit in french. In short, we reapply the emission factors from USEEIO (so specific to the US) to all countries of EXIOBASE, which has obvious limitations, but can constitute a good-enough estimation in some cases. (Agez et al., 2021) and (Muller, 2019) for example, show that these additional environmental extensions have a significant effect on some impact categories, namely, ecotoxicity and ozone layer depletion. In other words, normal environmental extensions of EXIOBASE are not equipped to study these impact categories. Other categories are marginally impacted (from 0.5 to 3% increase of the impact when looking at the whole economy's impact). Version of IW+ used: 10.5281/zenodo.3521034
description: {"references": ["Agez, M., Muller, E., Patouillard, L., Arvesen, A., Margni, M., Samson, R., & Majeau-Bettez, G. (2021). Correcting remaining truncations in hybrid LCA database compilation. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.13132", "Muller, E. (2019). Estimation et compl\u00e9tion des flux \u00e9l\u00e9mentaires manquants du tableau entr\u00e9es-sorties multir\u00e9gional EXIOBASE. Polytechnique Montreal. Retrieved from https://publications.polymtl.ca/3920/", "Bulle, C., Margni, M., Patouillard, L., Boulay, A. M., Bourgault, G., De Bruille, V., \u2026 Jolliet, O. (2019). IMPACT World+: a globally regionalized life cycle impact assessment method. International Journal of Life Cycle Assessment, 1653\u20131674. https://doi.org/10.1007/s11367-019-01583-0"]}
DOI: 10.21227/0ds0-vb44
title: Day-Ahead Forecasting using LSTM with rolling windows

description: The project involves day-ahead forecasting which takes a model fit into historical data and predicts future demand of electricity. Time series forecasting methods are used as the dataset contains an explicit order dependence between observations, i.e. a time dimension which makes the problem difficult to handle but makes the prediction more accurate. Various machine learning and deep learning algorithms were used to accomplish this work. Results obtained by autoregressive integrated moving average(ARIMA), autoregressive moving average with explanatory variable(ARIMAX), seasonal autoregressive moving average(SARIMA), seasonal autoregressive moving average(SARIMAX) and long short term memory(LSTM) recurrent neural networks with rolling windows were compared and the one with least error, i.e. LSTM with rolling windows was used for time series prediction.
DOI: 10.5281/zenodo.4662925
title: Ooh Na Na Definitions

description: A dump of prefix-identifier-definition strings as TSV (quoted using python's CSV module). Original data licensed under CC0, data from respective databases licensed the same as theirs.
DOI: 10.6084/m9.figshare.14371754
title: SRR13785025.broad_peaks.bw

description: BigWig file for group project
DOI: 10.6084/m9.figshare.14371754.v1
title: SRR13785025.broad_peaks.bw

description: BigWig file for group project
DOI: 10.6084/m9.figshare.14371757
title: SRR13785024.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.6084/m9.figshare.14371757.v1
title: SRR13785024.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.6084/m9.figshare.14371763
title: SRR13785021.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.6084/m9.figshare.14371763.v1
title: SRR13785021.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.6084/m9.figshare.14371766.v1
title: SRR13785020.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.6084/m9.figshare.14371766
title: SRR13785020.broad_peaks.bw

description: BIOINF545 Project BigWig
DOI: 10.25386/genetics.14349062
title: Supplemental Material for Yang et al., 2021

description: Supplemental Figure S1, Table S1 and Table S2
DOI: 10.25386/genetics.14349062.v1
title: Supplemental Material for Yang et al., 2021

description: Supplemental Figure S1, Table S1 and Table S2
DOI: 10.6084/m9.figshare.14183090
title: Fluorimeter_Fig6C-D_original_data

description: Original data set for Figure 6C-D: Fig 6C:The fluorimetric calibration curve showing the intensity of the fluorescence emission as a function of bead concentration for 6 independent biological replicates (Replicate 1-3: Infected cells, Replicate 4-6: Non-infected cells). Fig 6D: Estimation of the bead concentration in each fraction calculated using the calibration curve in C.
DOI: 10.26300/h1df-t614
title: Screening for Bacterial Vaginosis in Pregnant Adolescents and Women to Prevent Preterm Delivery
DOI: 10.5281/zenodo.4662943
title: Estadísticas de las carreras de Fórmula 1 desde sus inicios hasta nuestros días

description: Dataset en el que encontramos el histórico de las carreras de Fórmula 1 desde sus inicios hasta nuestros días, viene representado por las siguientes variables: RACE: nombre del gran premio. DATE: fecha en la que se disputó la carrera. NATIONALITY_WINNER: nacionalidad del ganador. WINNER: nombre y apellido del ganador. NATIONALITY_TEAM: nacionalidad de la escudería. TEAM: nombre de la escudería. LAPS: vueltas que se corrieron. TIME: duración total de la carrera (horas:minutos:segundos.milisegundos). TIME_PER_LAP: tiempo por vuelta (minutos:segundos.milisegundos).
DOI: 10.5281/zenodo.4647562
title: Estadísticas de las carreras de Fórmula 1 desde sus inicios hasta nuestros días

description: Dataset en el que encontramos el histórico de las carreras de Fórmula 1 desde sus inicios hasta nuestros días, viene representado por las siguientes variables: RACE: nombre del gran premio. DATE: fecha en la que se disputó la carrera. NATIONALITY_WINNER: nacionalidad del ganador. WINNER: nombre y apellido del ganador. NATIONALITY_TEAM: nacionalidad de la escudería. TEAM: nombre de la escudería. LAPS: vueltas que se corrieron. TIME: duración total de la carrera (horas:minutos:segundos.milisegundos). TIME_PER_LAP: tiempo por vuelta (minutos:segundos.milisegundos).
DOI: 10.5281/zenodo.4629979
title: Instance data for crane scheduling for end-of-aisle picking

description: The following instances are from the working paper entitled <em>Crane scheduling for end-of-aisle picking: Complexity and efficient solutions based on the vehicle routing problem</em><em> </em>by Nils Boysen, Konrad Stephan, and Simon Emde. The dataset contains 760 randomly generated instances with varying numbers of requests and buffer sizes. Each line in the attached text file stands for one instance. Columns are separated by tab stops. The individual columns are: ID - unique running number n - number of requests in set S b - buffer size, number of requests in set S_0 S_0 - set of request locations initially in the buffer S - set of request locations FCFS_SOLUTION - sequence vector (solution) in a first come, first served schedule FCFS_TIME - CPU time (milliseconds) for finding the first come, first served solution FCFS_OBJECTIVE - objective value (tenths of a second of empty crane travel) of the first come, first served solution MBH_SOLUTION - sequence vector (solution) found by the matching-based heuristic MBH_TIME - CPU time (milliseconds) taken by the matching-based heuristic MBH_OBJECTIVE - objective value (tenths of a second of empty crane travel) of the solution found by the matching-based heuristic VRP_SOLUTION - sequence vector (solution) of the VRP* (via HGA-CVRP) schedule VRP_TIME - CPU time (milliseconds) for finding the VRP* solution VRP_OBJECTIVE - objective value (tenths of a second of empty crane travel) of the VRP* solution Note that location data is in the format of two-dimensional coordinates denoting the travel time on the x- and y-axes in tenths of a second from coordinate (0,0). Hence coordinate "(600, 300)" implies that the corresponding slot is 60 seconds of horizontal travel and 30 seconds of vertical travel away from the origin. Objective values are also scaled to tenths of a second. The CPU times are given in milliseconds.
DOI: 10.5281/zenodo.3946879
title: ODP Leg 168, Hole 1032A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 168: Hydrothermal Circulation in the Oceanic Crust: Eastern Flank of the Juan de Fuca Ridge
DOI: 10.5281/zenodo.3946878
title: ODP Leg 168, Hole 1032A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 168: Hydrothermal Circulation in the Oceanic Crust: Eastern Flank of the Juan de Fuca Ridge
DOI: 10.5281/zenodo.3946883
title: ODP Leg 169, Hole 856H - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946882
title: ODP Leg 169, Hole 856H - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946885
title: ODP Leg 169, Hole 858G - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946884
title: ODP Leg 169, Hole 858G - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946881
title: ODP Leg 169, Hole 1037B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946880
title: ODP Leg 169, Hole 1037B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 169: Sedimented Ridges II
DOI: 10.5281/zenodo.3946887
title: ODP Leg 170, Hole 1039D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946886
title: ODP Leg 170, Hole 1039D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.4662959
title: Velocity field used for PlanktonIndividuals.jl

description: This is the velocity field used for the examples in PlanktonIndividuals.jl
DOI: 10.5281/zenodo.3946889
title: ODP Leg 170, Hole 1040C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946888
title: ODP Leg 170, Hole 1040C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946891
title: ODP Leg 170, Hole 1040D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946890
title: ODP Leg 170, Hole 1040D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946893
title: ODP Leg 170, Hole 1040E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946892
title: ODP Leg 170, Hole 1040E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946895
title: ODP Leg 170, Hole 1042C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946894
title: ODP Leg 170, Hole 1042C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946897
title: ODP Leg 170, Hole 1043B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.5281/zenodo.3946896
title: ODP Leg 170, Hole 1043B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 170: Costa Rica Accretionary Wedge
DOI: 10.6084/m9.figshare.14294558
title: Use of, and confidence in, evidence syntheses

description: Survey of 84 senior environmental professionals in Canada working at the science-policy interface, exploring their confidence in, and use of, evidence syntheses within their organizations.
DOI: 10.5281/zenodo.3946901
title: ODP Leg 171A, Hole 1044A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 171A: Northern Barbados Accretionary Prism - Logging While Drilling
DOI: 10.6084/m9.figshare.14294558.v1
title: Use of, and confidence in, evidence syntheses

description: Survey of 84 senior environmental professionals in Canada working at the science-policy interface, exploring their confidence in, and use of, evidence syntheses within their organizations.
DOI: 10.5281/zenodo.3946900
title: ODP Leg 171A, Hole 1044A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 171A: Northern Barbados Accretionary Prism - Logging While Drilling