cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1243359.v2
title: Punctuated distribution of recombination hotspots and demarcation of pericentromeric regions in Phaseolus vulgaris L.

description: <strong>Data:</strong> Polymorphic sequences between common bean genotypes CALIMA and JAMAPA, generated through genotyping-by-sequencing approach. <strong>2014 August</strong>
DOI: 10.6084/m9.figshare.1243359.v1
title: Punctuated distribution of recombination hotspots and demarcation of pericentromeric regions revealed by a high resolution linkage map of Phaseolus vulgaris L.

description: <strong>Data:</strong> Polymorphic sequences between common bean genotypes CALIMA and JAMAPA, generated through genotyping-by-sequencing approach. <strong>2014 August</strong>
DOI: 10.6084/m9.figshare.1243367.v2
title: COMENTARIO DE TRAMA DEL AMIANTO

description: JOSE.MONSERRATE_TRAMADELAMIANTO.DOC
DOI: 10.6084/m9.figshare.1243318
title: SUPPLEMENT: A REASSESSMENT OF MARÍA DE LA CRUZ CAVE SITE, PUERTO RICO: THE 2012 EXCAVATIONS

description: DATA SET: Cotains additional figures, data tables (mirco- and macro-artifacts), radiocarbon date calibrations, and a report of the analysis of an archaeological Sapotacea seed by L.A. Newsom to supplement the paper "A Reassessemnt of Maria de La Cruz Site, Puerto Rico: The 2012 Excavations to be published in:<em> Proceedings of the 25th International Congress of the Association of Caribbean Archaeology. San Juan: </em>IACA, 2015.
DOI: 10.6084/m9.figshare.1243316
title: Data for Stern (2013) Journal of Economic Literature

description: Citation data used in: Stern D. I. (2013) Uncertainty measures for economics journal impact factors, Journal of Economic Literature 51(1), 173-189.
DOI: 10.6084/m9.figshare.1243292
title: Newborn Skull Networks JASs 2015

description: Published in Esteve-Altava B, Rasskin-Gutman D. 2015. Evo-Devo Insights from Pathological Networks: Exploring Craniosynostosis as a Developmental Mechanism for Modularity and Complexity in the Human Skull. Journal of Anthropological Sciences 93: 1-15 DOI 10.4436/JASS.9300.
DOI: 10.6084/m9.figshare.1243316.v1
title: Data for Stern (2013) Journal of Economic Literature

description: Citation data used in: Stern D. I. (2013) Uncertainty measures for economics journal impact factors, Journal of Economic Literature 51(1), 173-189.
DOI: 10.6084/m9.figshare.1243200
title: Schroer_and_Wood_2015_Evolution_character_displacement_fossil_hominins_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_fossil_hominins_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 fossil hominins for character displacement analysis. Rows: 64 (header = TRUE) Columns: 15 Empty cell label: NA Variables: I, T, L, Intrx, Taxon, logmd, logbl, logarea, Randomly.selected?, ACC, Specific.taxon, P4MD, P4BL, P4AREA, P4AREACALC ==================== 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: placement of each specimen in one of four categories: EA.Homo, EA.Paranthropus, O.Homo, or S.Paranthropus. Categories are mutually exclusive. “EA” stands for East Africa, “O” stands for other, and the “S” in S.Paranthropus stands for South Africa. logmd, logbl, and logarea: the natural logs of the measurements P4MD, P4BL, and P4AREACALC. 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 Specific.taxon: genus and species name 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). Some fossil specimens were not available for this measure, in which case P4AREA is filled with “NA.” P4AREACALC. For fossil specimens without a P4AREA measurement, P4MD and P4BL are multiplied to give an estimate of P4AREA. P4AREA and P4AREACALC are the same for specimens available for measurements in tpsDIG.
DOI: 10.6084/m9.figshare.1243292.v1
title: Skull networks of newborns with and without craniosynostosisNewborn skull networks

description: Published in Esteve-Altava B, Rasskin-Gutman D. 2015. Evo-Devo Insights from Pathological Networks: Exploring Craniosynostosis as a Developmental Mechanism for Modularity and Complexity in the Human Skull. Journal of Anthropological Sciences 93: 1-15 DOI 10.4436/JASS.9300.
DOI: 10.6084/m9.figshare.1243186
title: Path Analysis & Structural Equation Modeling Code & Data

description: Code and data for Introduction to Path Analysis and Structural Equation Modeling workshop (Michel 2014, http://dx.doi.org/10.6084/m9.figshare.1243187)
DOI: 10.6084/m9.figshare.1243200.v1
title: Schroer_and_Wood_2015_Evolution_character_displacement_fossil_hominins_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_fossil_hominins_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 fossil hominins for character displacement analysis. Rows: 64 (header = TRUE) Columns: 15 Empty cell label: NA Variables: I, T, L, Intrx, Taxon, logmd, logbl, logarea, Randomly.selected?, ACC, Specific.taxon, P4MD, P4BL, P4AREA, P4AREACALC ==================== 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: placement of each specimen in one of four categories: EA.Homo, EA.Paranthropus, O.Homo, or S.Paranthropus. Categories are mutually exclusive. “EA” stands for East Africa, “O” stands for other, and the “S” in S.Paranthropus stands for South Africa. logmd, logbl, and logarea: the natural logs of the measurements P4MD, P4BL, and P4AREACALC. 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 Specific.taxon: genus and species name 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). Some fossil specimens were not available for this measure, in which case P4AREA is filled with “NA.” P4AREACALC. For fossil specimens without a P4AREA measurement, P4MD and P4BL are multiplied to give an estimate of P4AREA. P4AREA and P4AREACALC are the same for specimens available for measurements in tpsDIG.
DOI: 10.6084/m9.figshare.1243292.v2
title: Newborn Skull Networks JASs 2015

description: Published in Esteve-Altava B, Rasskin-Gutman D. 2015. Evo-Devo Insights from Pathological Networks: Exploring Craniosynostosis as a Developmental Mechanism for Modularity and Complexity in the Human Skull. Journal of Anthropological Sciences 93: 1-15 DOI 10.4436/JASS.9300.
DOI: 10.6084/m9.figshare.1243183
title: GIS Layer of Standardized Fish Mercury Concentrations Across Canada Now Available

description: Society of Environmental Toxicology and Chemistry North America Conference 2014 (Vancouver, Canada) Poster presentation. Poster Abstract: GIS Layer of Standardized Fish Mercury Concentrations Across Canada Now Available A national GIS layer of standardized fish mercury (Hg) data would be useful to the scientific community for many types of spatial analyses and modeling. To produce this national GIS layer for Canada, we first assembled all available fish Hg data across the country and did quality-assurance checks, which resulted in 387,872 fish Hg records. We removed all records from sites with known point-source Hg inputs or from hydroelectric reservoirs. The resulting dataset contained 231,063 records from 3547 locations across Canada from 1967-2010. We used this data and the USGS National Descriptive Model for Mercury in Fish (NDMMF) to estimate Hg concentrations in a standard-length (12-cm) whole yellow perch for each sampling location. The resulting geocoded dataset of standardized fish Hg concentrations is called the Fish Mercury Datalayer for Canada, or FIMDAC, and is now available to the scientific community. FIMDAC can be used for Hg eco-risk assessment, modeling of Hg dynamics and bioaccumulation in aquatic ecosystems, spatial analysis of Hg biogeochemistry, and as baseline for modeling future Hg management scenarios and their environmental consequences. FIMDAC is not appropriate for human health risk assessment, temporal trend analysis or for setting human consumption guidelines. Relevant links: Data request form: http://www.smu.ca/research/fish-mercury-datalayer.html Metadata: http://dx.doi.org/10.6084/m9.figshare.1210773
DOI: 10.6084/m9.figshare.1243184
title: General and Generalized Linear Mixed Models

description: Code and data for General and Generalized Linear Mixed Models workshop (Michel 2014, slides available here: http://dx.doi.org/10.6084/m9.figshare.1243185)
DOI: 10.6084/m9.figshare.1243186.v1
title: Path Analysis & Structural Equation Modeling Code & Data

description: Code and data for Introduction to Path Analysis and Structural Equation Modeling workshop (Michel 2014, http://dx.doi.org/10.6084/m9.figshare.1243187)
DOI: 10.6084/m9.figshare.1243200.v2
title: FILESET_Schroer_and_Wood_2015_Evolution_character_displacement_fossil_hominins_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_fossil_hominins_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 fossil hominins for character displacement analysis. Rows: 64 (header = TRUE) Columns: 15 Empty cell label: NA Variables: I, T, L, Intrx, Taxon, logmd, logbl, logarea, Randomly.selected?, ACC, Specific.taxon, P4MD, P4BL, P4AREA, P4AREACALC ==================== 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: placement of each specimen in one of four categories: EA.Homo, EA.Paranthropus, O.Homo, or S.Paranthropus. Categories are mutually exclusive. “EA” stands for East Africa, “O” stands for other, and the “S” in S.Paranthropus stands for South Africa. logmd, logbl, and logarea: the natural logs of the measurements P4MD, P4BL, and P4AREACALC. 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 Specific.taxon: genus and species name 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). Some fossil specimens were not available for this measure, in which case P4AREA is filled with “NA.” P4AREACALC. For fossil specimens without a P4AREA measurement, P4MD and P4BL are multiplied to give an estimate of P4AREA. P4AREA and P4AREACALC are the same for specimens available for measurements in tpsDIG.
DOI: 10.6084/m9.figshare.1243152
title: دراسة لإنتاج خزف الأسنان الظليل

description: Opaque Dental Porcelain
DOI: 10.6084/m9.figshare.1243153
title: Study to Produce Coloured Opaque Dental Porcelain

description: Coloured Opaque Dental Porcelain.
DOI: 10.6084/m9.figshare.1243184.v1
title: General and Generalized Linear Mixed Models

description: Code and data for General and Generalized Linear Mixed Models workshop (Michel 2014, slides available here: http://dx.doi.org/10.6084/m9.figshare.1243185)
DOI: 10.6084/m9.figshare.1243151
title: Complete Dentures

description: Complete dentures for dental technology students.
DOI: 10.6084/m9.figshare.1243150
title: Modulus of Rupture and Hardness of Opaque Dental

description: Dental porcelain
DOI: 10.6084/m9.figshare.1243153.v1
title: Study to Produce Coloured Opaque Dental Porcelain

description: Coloured Opaque Dental Porcelain.
DOI: 10.6084/m9.figshare.1243142
title: Dental Laboratory Safety السلامة في معامل الأسنان

description: Presentation about safety in dental laboratories.
DOI: 10.6084/m9.figshare.1243150.v1
title: Modulus of Rupture and Hardness of Opaque Dental

description: Dental porcelain
DOI: 10.6084/m9.figshare.1241683
title: POSTER ESTRES HMHA

description: ESTRES EN EN SALA DE EMERGENCIA DEL HMHA
DOI: 10.6084/m9.figshare.1241650
title: ICBM_aging_connectome

description: Some region-level connectomes for the ICBM aging dataset preprocessed with NIAK
DOI: 10.6084/m9.figshare.1241683.v1
title: POSTER ESTRES HMHA

description: ESTRES EN EN SALA DE EMERGENCIA DEL HMHA
DOI: 10.6084/m9.figshare.1241597
title: Evaluating the Accuracy and Precision of Multiple Abundance Estimators Using State-Space Models: A Case Study for a Threatened Population of Chinook Salmon in Johnson Creek, Idaho

description: Over the last century, Chinook Salmon <i>Oncorhynchus tshawytscha</i> populations in the Pacific Northwest have experienced dramatic declines, leading to many of them being listed as threatened under the Endangered Species Act. The abundance of these threatened populations relative to the thresholds for delisting remains the primary metric for assessing recovery, yet determining the true population abundances from multiple survey types with unknown levels of accuracy and precision remains difficult. The abundance of the spring–summer Chinook Salmon population in Johnson Creek, Idaho, has been measured using a mark–recapture survey and three different redd count surveys (RCSs) that vary temporally and spatially. Using a state-space model, we determined the accuracy and precision of each survey type by decoupling the observation error of the survey from the process error describing the annual variability in the true population abundance. We then extended the results of the model to determine the risk of managers’ incorrectly delisting the population (a type I error) or incorrectly keeping it listed (a type II error). Finally, we show that salmon managers with data-limited populations (primarily those with only single-pass index RCSs) might use the results of our risk analysis to determine whether expanding survey efforts to minimize management risks is appropriate when they are confronted with dwindling financial resources. For example, we determined that although both the multiple-pass extended RCS (CV = 0.06) and mark–recapture surveys (CV = 0.14) provide unbiased estimates of salmon abundance in Johnson Creek, the mark–recapture study can have annual costs that are 30–100 times greater. Managers may determine that directing research funds toward acquiring information unique to weir-based mark–recapture surveys (i.e., migration timing, good genetics samples, etc.) may not be justified for all populations.Received October 7, 2013; accepted March 5, 2014
DOI: 10.6084/m9.figshare.1241597.v1
title: Evaluating the Accuracy and Precision of Multiple Abundance Estimators Using State-Space Models: A Case Study for a Threatened Population of Chinook Salmon in Johnson Creek, Idaho

description: Over the last century, Chinook Salmon <i>Oncorhynchus tshawytscha</i> populations in the Pacific Northwest have experienced dramatic declines, leading to many of them being listed as threatened under the Endangered Species Act. The abundance of these threatened populations relative to the thresholds for delisting remains the primary metric for assessing recovery, yet determining the true population abundances from multiple survey types with unknown levels of accuracy and precision remains difficult. The abundance of the spring–summer Chinook Salmon population in Johnson Creek, Idaho, has been measured using a mark–recapture survey and three different redd count surveys (RCSs) that vary temporally and spatially. Using a state-space model, we determined the accuracy and precision of each survey type by decoupling the observation error of the survey from the process error describing the annual variability in the true population abundance. We then extended the results of the model to determine the risk of managers’ incorrectly delisting the population (a type I error) or incorrectly keeping it listed (a type II error). Finally, we show that salmon managers with data-limited populations (primarily those with only single-pass index RCSs) might use the results of our risk analysis to determine whether expanding survey efforts to minimize management risks is appropriate when they are confronted with dwindling financial resources. For example, we determined that although both the multiple-pass extended RCS (CV = 0.06) and mark–recapture surveys (CV = 0.14) provide unbiased estimates of salmon abundance in Johnson Creek, the mark–recapture study can have annual costs that are 30–100 times greater. Managers may determine that directing research funds toward acquiring information unique to weir-based mark–recapture surveys (i.e., migration timing, good genetics samples, etc.) may not be justified for all populations.Received October 7, 2013; accepted March 5, 2014
DOI: 10.6084/m9.figshare.1241650.v1
title: ICBM_aging_connectome

description: Some region-level connectomes for the ICBM aging dataset preprocessed with NIAK
DOI: 10.6084/m9.figshare.1241586
title: What Happens Depends on When It Happens: Copula-Based Ordered Event History Analysis of Civil War Duration and Outcome

description: Scholars are interested in not just what event happens but also when the event happens. If there is dependence among events or dependence between time and events, however, the currently common methods (e.g., competing risks approaches) produce biased estimates. To deal with these problems, this article proposes a new method of copula-based ordered event history analysis (COEHA). A merit of working with copulas is that, whatever marginal distributions time and event variables follow (including the Cox model), researchers can derive whatever joint distribution exists between the two. Application of the COEHA model to a dataset from civil wars supports two controversial hypotheses. First, as wars become longer, rebel victory becomes more likely but settlement does not (there is dependence between time and events at both tails). Second, stronger rebels make wars shorter but do not necessarily tend to win, as experts predict but fail to establish (rebels’ strength shortens time but has no effect on which events occur). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1241586.v2
title: What Happens Depends on When It Happens: Copula-Based Ordered Event History Analysis of Civil War Duration and Outcome

description: Scholars are interested in not just what event happens but also when the event happens. If there is dependence among events or dependence between time and events, however, the currently common methods (e.g., competing risks approaches) produce biased estimates. To deal with these problems, this article proposes a new method of copula-based ordered event history analysis (COEHA). A merit of working with copulas is that, whatever marginal distributions time and event variables follow (including the Cox model), researchers can derive whatever joint distribution exists between the two. Application of the COEHA model to a dataset from civil wars supports two controversial hypotheses. First, as wars become longer, rebel victory becomes more likely but settlement does not (there is dependence between time and events at both tails). Second, stronger rebels make wars shorter but do not necessarily tend to win, as experts predict but fail to establish (rebels’ strength shortens time but has no effect on which events occur). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1241583
title: Likelihood Estimation for the INAR(<i>p</i>) Model by Saddlepoint Approximation

description: Saddlepoint techniques have been used successfully in many applications, owing to the high accuracy with which they can approximate intractable densities and tail probabilities. This article concerns their use for the estimation of high-order integer-valued autoregressive, INAR(<i>p</i>), processes. Conditional least squares estimation and maximum likelihood estimation have been proposed for INAR(<i>p</i>) models, but the first is inefficient for estimating parametric models, and the second becomes difficult to implement as the order <i>p</i> increases. We propose a simple saddlepoint approximation to the log-likelihood that performs well even in the tails of the distribution and with complicated INAR models. We consider Poisson and negative binomial innovations, and show empirically that the estimator that maximises the saddlepoint approximation behaves very similarly to the maximum likelihood estimator in realistic settings. The approach is applied to data on meningococcal disease counts. Supplementary materials for this article are available online.