cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1404224.v1
title: Economic and Mobility dataset.

description: In this dataset, the economic and mobility variables are combined.
DOI: 10.6084/m9.figshare.1404210
title: Sub17

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404216
title: Wadi Hasa Ancient Pastoralism Project, Data Archive

description: These are data files related to older surveys done in the Wadi al-Hasa region of central Jordan. There are some new data too.
DOI: 10.6084/m9.figshare.1404222.v1
title: Mobility and economy for CCA, unsconstrained variables

description: These are the unconstrained variables for a CCA analysis of economy and mobility variables.
DOI: 10.6084/m9.figshare.1404226.v3
title: Economic, mobility, and environmental dataset

description: In this dataset, the economic, mobility, and environmental data have been combined.
DOI: 10.6084/m9.figshare.1404226.v2
title: Economic, Mobility, and Environmental dataset

description: In this dataset, the economic, mobility, and environmental data have been combined.
DOI: 10.6084/m9.figshare.1404209
title: Sub16

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404210.v1
title: Sub17

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404216.v2
title: Wadi Hasa Ancient Pastoralism Project, Data Archive

description: These are data files related to older surveys done in the Wadi al-Hasa region of central Jordan. There are some new data too.
DOI: 10.6084/m9.figshare.1404209.v1
title: Sub16

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404200
title: Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients: SFC Model

description: The spatial foraging conditions (SFC) models grouped foraging observations over time at a fixed cell and integrated spatial patterns (e.g., topography) unaccounted for by the chosen set of hydrological predictors (i.e., spatial correlation; Beerens et al, in press). By accounting for patterns in the spatial variation of the landscape, the noise independent of the hydrologic parameters can be reduced to better capture the species-specific behavioral response to rapidly changing habitat conditions (Dormann 2007). In addition to depth, recession, and DSD, the SFC used dry-to-wet reversal, and hydroperiod, averaged over each instance of cell use. The dry-to-wet reversal variable estimated when a cell had gone dry and rewet (within a dry season), which results in highly depleted fish populations (Trexler et al. 2002). Hydroperiod was defined as the 10-year mean in the annual length of cell inundation, which influences wading bird distributions through long-term changes in microtopography and vegetation communities. Frequency of cell use was defined as the number of times over the study period that a species used a given cell. Output from the SFC averaged over the landscape can serve as a surrogate measure of the abundance of high-quality patches.
DOI: 10.6084/m9.figshare.1404206
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: Avital Sternin; Sebastian Stober; Jessica A. Grahn & Adrian M. Owen. <em>"Tempo Estimation from the EEG Signal during Perception and Imagination of Music."</em> In: 1st International Workshop on Brain-Computer Music Interfacing / 11th International Symposium on Computer Music Multidisciplinary Research (BCMI/CMMR’15), 2015.
DOI: 10.6084/m9.figshare.1404208
title: Sub15

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404216.v1
title: Wadi Hasa Ancient Pastoralism Project

description: These are data files related to older surveys done in the Wadi al-Hasa region of central Jordan. There are some new data too.
DOI: 10.6084/m9.figshare.1404208.v1
title: Sub15

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404199
title: Sub14

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404199.v1
title: Sub14

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404198
title: Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients: TFC Model

description: The temporal foraging conditions (TFC) models used daily mean hydrological characteristics calculated throughout the Everglades. Three explanatory variables representing hydrological conditions across a gradient of temporal scales were used as proxies for prey dynamics: days since drydown (DSD) was used as an indicator for long-term prey production (Trexler 2010), recession rate was used as an indicator for prey concentration dynamics (Russell et al. 2002, Beerens et al. 2011), and daily water depth was used as an indicator of short-term prey availability (Gawlik 2002, Beerens et al. 2011). These temporally-specific “resources” were considered available only when cell depths were in the foraging depth range of each species (Beerens et al, in press). The mean levels and heterogeneity (SD) of each resource were used to predict resource selection, which subsequently was used to predict the abundance of flocks and individuals across the landscape. Flock presence was defined as one or more birds of the target species (e.g., egrets, ibises, and storks) detected in a cell, whereas individual abundance counted the total number of birds present. Both individual and flock responses were modeled because wading birds are highly social and select foraging habitat based in part on the presence of conspecifics, a process that may increase or decrease individual fitness (Campomizzi et al. 2008). For the TFC, daily output summed over each region (Water Conservation Area (WCA) 1, WCA-2, WCA-3N, WCA-3S, Big Cypress National Park (BCNP), and Everglades National Park (ENP); Fig. 1) represents the mean patch quality of the landscape, within the suitable water depths of each species.
DOI: 10.6084/m9.figshare.1404198.v1
title: Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients: TFC Model

description: The temporal foraging conditions (TFC) models used daily mean hydrological characteristics calculated throughout the Everglades. Three explanatory variables representing hydrological conditions across a gradient of temporal scales were used as proxies for prey dynamics: days since drydown (DSD) was used as an indicator for long-term prey production (Trexler 2010), recession rate was used as an indicator for prey concentration dynamics (Russell et al. 2002, Beerens et al. 2011), and daily water depth was used as an indicator of short-term prey availability (Gawlik 2002, Beerens et al. 2011). These temporally-specific “resources” were considered available only when cell depths were in the foraging depth range of each species (Beerens et al, in press). The mean levels and heterogeneity (SD) of each resource were used to predict resource selection, which subsequently was used to predict the abundance of flocks and individuals across the landscape. Flock presence was defined as one or more birds of the target species (e.g., egrets, ibises, and storks) detected in a cell, whereas individual abundance counted the total number of birds present. Both individual and flock responses were modeled because wading birds are highly social and select foraging habitat based in part on the presence of conspecifics, a process that may increase or decrease individual fitness (Campomizzi et al. 2008). For the TFC, daily output summed over each region (Water Conservation Area (WCA) 1, WCA-2, WCA-3N, WCA-3S, Big Cypress National Park (BCNP), and Everglades National Park (ENP); Fig. 1) represents the mean patch quality of the landscape, within the suitable water depths of each species.
DOI: 10.6084/m9.figshare.1404195
title: Sub13

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404200.v1
title: Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients

description: The spatial foraging conditions (SFC) models grouped foraging observations over time at a fixed cell and integrated spatial patterns (e.g., topography) unaccounted for by the chosen set of hydrological predictors (i.e., spatial correlation; Beerens et al, in press). By accounting for patterns in the spatial variation of the landscape, the noise independent of the hydrologic parameters can be reduced to better capture the species-specific behavioral response to rapidly changing habitat conditions (Dormann 2007). In addition to depth, recession, and DSD, the SFC used dry-to-wet reversal, and hydroperiod, averaged over each instance of cell use. The dry-to-wet reversal variable estimated when a cell had gone dry and rewet (within a dry season), which results in highly depleted fish populations (Trexler et al. 2002). Hydroperiod was defined as the 10-year mean in the annual length of cell inundation, which influences wading bird distributions through long-term changes in microtopography and vegetation communities. Frequency of cell use was defined as the number of times over the study period that a species used a given cell. Output from the SFC averaged over the landscape can serve as a surrogate measure of the abundance of high-quality patches.
DOI: 10.6084/m9.figshare.1404206.v1
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: <em>"Tempo Estimation from the EEG signal during Perception and Imagination of Music"</em> This is a placeholder that will be updated once the paper is published.
DOI: 10.6084/m9.figshare.1404195.v1
title: Sub13

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404206.v2
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: <em>"Tempo Estimation from the EEG signal during Perception and Imagination of Music"</em> This is a placeholder that will be updated once the paper is published.
DOI: 10.6084/m9.figshare.1404183
title: Sub12

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404182
title: Sub11

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404183.v1
title: Sub12

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404181
title: Oil and Gas Exploration Valuation and the Value of Waiting

description: The timing flexibility of investments in oil and gas assets can potentially add value. In this article, we examine the value of waiting in exploration projects and propose a real option–based valuation method using least-squares Monte Carlo simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait, especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two-factor stochastic price process. The article also presents the valuation method in the form of MATLAB functions and routines that can be used as an efficient test and analysis platform using the industry-standard input formats.
DOI: 10.6084/m9.figshare.1404182.v1
title: Sub11

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404206.v3
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: <em>"Tempo Estimation from the EEG signal during Perception and Imagination of Music"</em>
DOI: 10.6084/m9.figshare.1404206.v4
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: <em>"Tempo Estimation from the EEG signal during Perception and Imagination of Music"</em>
DOI: 10.6084/m9.figshare.1404181.v1
title: Oil and Gas Exploration Valuation and the Value of Waiting

description: The timing flexibility of investments in oil and gas assets can potentially add value. In this paper we examine the value of waiting in exploration projects and propose a real option based valuation method using the Least Squares Monte Carlo Simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait; especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two–factor stochastic price process. The paper also presents the valuation method in the form of MATLAB® functions and routines which can be used as an efficient test and analysis platform using the industry-standard input formats.
DOI: 10.6084/m9.figshare.1404206.v5
title: Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

description: Supplementary Material for the paper: <em>"Tempo Estimation from the EEG signal during Perception and Imagination of Music"</em>
DOI: 10.6084/m9.figshare.1404181.v3
title: Oil and Gas Exploration Valuation and the Value of Waiting

description: The timing flexibility of investments in oil and gas assets can potentially add value. In this article, we examine the value of waiting in exploration projects and propose a real option–based valuation method using least-squares Monte Carlo simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait, especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two-factor stochastic price process. The article also presents the valuation method in the form of MATLAB functions and routines that can be used as an efficient test and analysis platform using the industry-standard input formats.
DOI: 10.6084/m9.figshare.1404181.v2
title: Oil and Gas Exploration Valuation and the Value of Waiting

description: The timing flexibility of investments in oil and gas assets can potentially add value. In this article, we examine the value of waiting in exploration projects and propose a real option–based valuation method using least-squares Monte Carlo simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait, especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two-factor stochastic price process. The article also presents the valuation method in the form of MATLAB functions and routines that can be used as an efficient test and analysis platform using the industry-standard input formats.
DOI: 10.6084/m9.figshare.1404171
title: Sub10

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404171.v1
title: Sub10

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404168
title: Sub08

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404167
title: Sub09

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404168.v1
title: Sub08

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404166
title: Sub07

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404167.v1
title: Sub09

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404157
title: Sub06

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404166.v1
title: Sub07

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404157.v1
title: Sub06

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404155
title: Sub04

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404156
title: Sub05

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404155.v1
title: Sub04

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404156.v1
title: Sub05

description: This archive file includes a de-identified rest-state fMRI data and two de-identified working-memory-task fMRI data (i.e., 2-back and 3-back).
DOI: 10.6084/m9.figshare.1404152
title: Schroer_and_Wood_2015_Evolution_character_displacement_macaques_v1

description: PREFERRED CITATION: Schroer K and Wood B. 2015. The role of character displacement in the molarization of hominin mandibular premolars. Evolution. DOI: 10.1111/evo.12672. Title: Schroer_and_Wood_2015_Evolution_character_displacement_macaques_v1 Version number: 1 Date (ISO) original data publication: 5/5/15 Dates (ISO) data collection: 01/01/12-01/01/14 Date (ISO) online publication: 5/5/15 Companion manuscript title: "The role of character displacement in the molarization of hominin mandibular premolars" Companion manuscript journal: Evolution Primary collector of data: Kes Schroer Co-authors: Bernard Wood Research assistants: Kristen Ramirez, Christine Foltz Short description: Measurements of the lower fourth premolar (P4) from macaques for character displacement analysis. Rows: 60 (header = TRUE) Columns: 17 Empty cell label: NA Variables: I, T, L, Intrx, Taxon, logmd, logbl, logarea, Randomly.selected?, ACC, Museum, Sex, Group, Location, P4MD, P4BL, P4AREA ==================== I, T, L, and Intrx: variables of the input matrix. “I” is Intercept and is always 1. “T” is “Taxon” and is encoded “1” for one taxon and “-1” for the other. “L” is location and is “1” for the sympatrix taxa and “-1” for the allopatric taxa. “Intrx” is the interaction of T and L and simply multiples these two values. Taxon: genus and species logmd, logbl, and logarea: the natural logs of the measurements P4MD, P4BL, and P4AREA. Randomly.selected: whether or not a specimen was randomly selected by the custom R script for character displacement analysis (see manuscript for more details). “Yes” indicates that the specimen was selected and “NA” indicates that the specimen was not. ACC: specimen number assigned by the host museum Museum: AMNH = American Museum of Natural History, NMNH = National Museum of Natural History, Smithsonian Institution, BMNH = Museum of Natural History, London Sex: F = female, M = male Group: “Macaques” for all specimens. This category has other values in other datasheets for the character displacement analysis, for easy combinations during comparative analyses. Location: NA for all specimens; column is only relevant to African ape sample in this analysis P4MD: mesiodistal diameter of the lower fourth premolar, in millimeters P4BL: buccolingual diameter of the lower fourth premolar, in millimeters P4AREA: area of the occlusal surface of the lower fourth premolar, calculated through a resampled outline in the digitization program tpsDIG (see manuscript for more details)