cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3514577
title: Association between time to progression and subsequent survival inceritinib-treated patients with advanced ALK-positive non-small-cell lung cancer

description: <b>Objective:</b> Time to progression (TTP) is a surrogate marker of overall survival (OS). However, OS is also dependent on post-progression survival (PPS). This study evaluated the association between TTP and the duration of PPS among adult patients who received ceritinib (Zykadia<sup>1</sup>) for the treatment of advanced anaplastic lymphoma kinase positive (ALK+) non-small-cell lung cancer (NSCLC). <b>Research design and methods:</b> A pooled analysis was performed on 181 ASCEND-1 (phase I) and ASCEND-2 (phase II) patients who experienced disease progression while on ceritinib. TTP was assessed on its association with PPS in a Kaplan–Meier analysis and in Cox proportional hazard models, adjusted for clinical covariates. <b>Main outcome measures:</b> Main outcomes measured include TTP, PPS, and OS. <b>Results:</b> Patients with TTP ≥6 months experienced significantly longer PPS compared to those with TTP &lt;6 months (median: 9.8 vs. 6.5 months, log-rank <i>p</i>-value <b>Conclusions:</b> A longer duration of TTP after treatment with ceritinib was significantly associated with a longer duration of both PPS and OS.
DOI: 10.6084/m9.figshare.3514577.v1
title: Association between time to progression and subsequent survival in ceritinib-treated patients with advanced ALK-positive non-small cell lung cancer

description: <b>Objective</b>: Time to progression (TTP) is a surrogate marker of overall survival (OS). However, OS is also dependent on post-progression survival (PPS). This study evaluated the association between TTP and the duration of PPS among adult patients who received ceritinib (Zykadia*) for the treatment of advanced anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC). <b>Research design and methods</b>: A pooled analysis was performed on 181 ASCEND-1 (Phase I) and ASCEND-2 (Phase II) patients who experienced disease progression while on ceritinib. TTP was assessed on its association with PPS in a Kaplan-Meier analysis and in Cox proportional hazard models, adjusted for clinical covariates. <b>Main outcome measures:</b> Main outcomes measured include TTP, PPS, and OS. <b>Results</b>: Patients with TTP ≥ 6 months experienced significantly longer PPS compared to those with TTP &lt; 6 months (median: 9.8 vs. 6.5 months, log-rank p-value &lt; 0.01). When TTP was assessed as a continuous variable, every 3 months of longer TTP was associated with a 21% lower hazard of death following progression (hazard ratio [HR]: 0.79, 95% confidence interval [CI]: 0.63–1.00; adjusted HR: 0.79, 95% CI: 0.64–0.99). This positive association translated into an OS benefit: each 3 months of longer TTP was associated with a lower hazard of death (adjusted HR: 0.46, 95% CI: 0.37–0.58). Median OS was 20.0 months for patients with TTP ≥ 6 months and was 10.9 months for patients with TTP &lt; 6 months. <b>Conclusions</b>: A longer duration of TTP after treatment with ceritinib was significantly associated with a longer duration of both PPS and OS.
DOI: 10.6084/m9.figshare.3514577.v2
title: Association between time to progression and subsequent survival inceritinib-treated patients with advanced ALK-positive non-small-cell lung cancer

description: <b>Objective:</b> Time to progression (TTP) is a surrogate marker of overall survival (OS). However, OS is also dependent on post-progression survival (PPS). This study evaluated the association between TTP and the duration of PPS among adult patients who received ceritinib (Zykadia<sup>1</sup>) for the treatment of advanced anaplastic lymphoma kinase positive (ALK+) non-small-cell lung cancer (NSCLC). <b>Research design and methods:</b> A pooled analysis was performed on 181 ASCEND-1 (phase I) and ASCEND-2 (phase II) patients who experienced disease progression while on ceritinib. TTP was assessed on its association with PPS in a Kaplan–Meier analysis and in Cox proportional hazard models, adjusted for clinical covariates. <b>Main outcome measures:</b> Main outcomes measured include TTP, PPS, and OS. <b>Results:</b> Patients with TTP ≥6 months experienced significantly longer PPS compared to those with TTP &lt;6 months (median: 9.8 vs. 6.5 months, log-rank <i>p</i>-value <b>Conclusions:</b> A longer duration of TTP after treatment with ceritinib was significantly associated with a longer duration of both PPS and OS.
DOI: 10.6084/m9.figshare.3514574
title: Supplement 1. R source code for model simulations.

description: File List growthpop.txt Description This is a text file containing R-language source code for the model simulations. This code calculates the number of mated females in gypsy moth populations. The first section describes the mate location model fitted to experimental data. It gives the probability of a male to find a female at a given distance and given day after its emergence (rate0 for day of emergence, rate1 for the following day and rate2 for the next one). The second section provides the distribution of male and female emergence days. The third section contains the main function growthpop. Entry variables are: Negg the number of eggs initially introduced, fec the number of eggs per female, mortrate the mortality from egg to adult, diflarv the larval diffusion coefficient, and difadult the adult diffusion coefficient for males. Output variables are: Mat the number of mated females of the second generation, eggs the number of eggs laid by females of second generation, growth the 2-year population growth rate, and Fcomp a matrix containing the identification number of the female in column 1, the coordinates in column 2-3, the day of emergence in column 4, the mating success (0: not mated; 1: mated) in column 5, and the mating probability in column 6.
DOI: 10.6084/m9.figshare.3514574.v1
title: Supplement 1. R source code for model simulations.

description: File List growthpop.txt Description This is a text file containing R-language source code for the model simulations. This code calculates the number of mated females in gypsy moth populations. The first section describes the mate location model fitted to experimental data. It gives the probability of a male to find a female at a given distance and given day after its emergence (rate0 for day of emergence, rate1 for the following day and rate2 for the next one). The second section provides the distribution of male and female emergence days. The third section contains the main function growthpop. Entry variables are: Negg the number of eggs initially introduced, fec the number of eggs per female, mortrate the mortality from egg to adult, diflarv the larval diffusion coefficient, and difadult the adult diffusion coefficient for males. Output variables are: Mat the number of mated females of the second generation, eggs the number of eggs laid by females of second generation, growth the 2-year population growth rate, and Fcomp a matrix containing the identification number of the female in column 1, the coordinates in column 2-3, the day of emergence in column 4, the mating success (0: not mated; 1: mated) in column 5, and the mating probability in column 6.
DOI: 10.6084/m9.figshare.3514571
title: Appendix A. Results of the exact Wilcoxon rank sum test for dbh distributions.

description: Results of the exact Wilcoxon rank sum test for dbh distributions.
DOI: 10.6084/m9.figshare.3514571.v1
title: Appendix A. Results of the exact Wilcoxon rank sum test for dbh distributions.

description: Results of the exact Wilcoxon rank sum test for dbh distributions.
DOI: 10.6084/m9.figshare.3514568
title: Appendix B. List of tree species and their light requirement.

description: List of tree species and their light requirement.
DOI: 10.6084/m9.figshare.3514568.v1
title: Appendix B. List of tree species and their light requirement.

description: List of tree species and their light requirement.
DOI: 10.6084/m9.figshare.3514565
title: Appendix C. Extended Fig. 5 with the development of dbh structure over time for light-demanding and shade-tolerant tree species.

description: Extended Fig. 5 with the development of dbh structure over time for light-demanding and shade-tolerant tree species.
DOI: 10.6084/m9.figshare.3514565.v1
title: Appendix C. Extended Fig. 5 with the development of dbh structure over time for light-demanding and shade-tolerant tree species.

description: Extended Fig. 5 with the development of dbh structure over time for light-demanding and shade-tolerant tree species.
DOI: 10.6084/m9.figshare.3514562
title: Appendix A. Parameters used for Leslie matrix model transition, birth, and initial conditions.

description: Parameters used for Leslie matrix model transition, birth, and initial conditions.
DOI: 10.6084/m9.figshare.3514562.v1
title: Appendix A. Parameters used for Leslie matrix model transition, birth, and initial conditions.

description: Parameters used for Leslie matrix model transition, birth, and initial conditions.
DOI: 10.6084/m9.figshare.3514559
title: Appendix A. Functional classification of bird species found in nine exclosure sites.

description: Functional classification of bird species found in nine exclosure sites.
DOI: 10.6084/m9.figshare.3514556
title: Appendix A. A table showing the frequency of occurrence for passerine birds detected during point-count surveys on the Cosumnes Reserve, California, USA, in 2004 and 2005.

description: A table showing the frequency of occurrence for passerine birds detected during point-count surveys on the Cosumnes Reserve, California, USA, in 2004 and 2005.
DOI: 10.6084/m9.figshare.3514556.v1
title: Appendix A. A table showing the frequency of occurrence for passerine birds detected during point-count surveys on the Cosumnes Reserve, California, USA, in 2004 and 2005.

description: A table showing the frequency of occurrence for passerine birds detected during point-count surveys on the Cosumnes Reserve, California, USA, in 2004 and 2005.
DOI: 10.6084/m9.figshare.3514553
title: Appendix B. A table showing the parameter estimates and area-under-the-curve values for the best logistic-regression model identified using stepwise model selection for 16 species of riparian bird species.

description: A table showing the parameter estimates and area-under-the-curve values for the best logistic-regression model identified using stepwise model selection for 16 species of riparian bird species.
DOI: 10.6084/m9.figshare.3514553.v1
title: Appendix B. A table showing the parameter estimates and area-under-the-curve values for the best logistic-regression model identified using stepwise model selection for 16 species of riparian bird species.

description: A table showing the parameter estimates and area-under-the-curve values for the best logistic-regression model identified using stepwise model selection for 16 species of riparian bird species.
DOI: 10.6084/m9.figshare.3514550
title: Appendix A. Persistence and yield plots at varying exploitation rates outside reserves.

description: Persistence and yield plots at varying exploitation rates outside reserves.
DOI: 10.6084/m9.figshare.3514550.v1
title: Appendix A. Persistence and yield plots at varying exploitation rates outside reserves.

description: Persistence and yield plots at varying exploitation rates outside reserves.
DOI: 10.6084/m9.figshare.3514547
title: Appendix A. Robustness analysis.

description: Robustness analysis.
DOI: 10.6084/m9.figshare.3514547.v1
title: Appendix A. Robustness analysis.

description: Robustness analysis.
DOI: 10.6084/m9.figshare.3514544
title: Supplement 1. Data from the 83 South American lakes sampled.

description: File List South_American_Lakes_data.txt -- data file Description The "South American Lakes data file" contains data on 83 shallow lakes sampled in South America between November 2004 and March 2006. The following data is presented: X coordinate (Decimal degrees); Y coordinate (Decimal degrees); name (Name of the lake. Not all lakes have a name and some lakes are referred to by multiple names); climate zone (The lakes were grouped in five different categories based on the prevailing climate characteristics following the Köppen climate system (1936) digitized by Leemans and Cramer (1991): tropicali, tropical, subtropical, maritime temperate and tundra zone. The Köppen classification is based on monthly rainfall and temperature. Tropicali is an isothermal subzone in the tropics, which has a smaller annual temperature range than the tropical zone, source:<br> http://www.fao.org/WAICENT/FAOINFO/SUSTDEV/EIdirect/climate/EIsp0002.htm); average air temperature in warmest month (Celcius, source: M. New, D. Lister, M. Hulme, and I. Makin. 2002. A high-resolution data set of surface climate over global land areas. Climate Research 21. The complete paper can be freely downloaded via:<br> http://www.cru.uea.ac.uk/cru/data/tmc.htm); average air temperature in coldest month (Celcius, source: M. New, D. Lister, M. Hulme, and I. Makin. 2002: A high-resolution data set of surface climate over global land areas. Climate Research 21. The complete paper can be freely downloaded via:<br> http://www.cru.uea.ac.uk/cru/data/tmc.htm); soil type (Source: SOTERLAC "Soil and terrain database for Latin America and the Caribbean ", FAO: Land and Water Digital Media Series #5, scale: 1:5 million scale. Type of top soil and descriptions of the different types can be found at:<br> http://www.fao.org/AG/agl/agll/key2soil.stm); lake area (m<sup>2</sup>, determined using landsat Orthorectified Landsat Thematic Mapper Mosaics of the year 2000. If image was cloudy images of 1990 were used. In rare cases when image deviated much from area observed in the field, all waypoints measured in the field were plotted on top of the image and a "best matching" polygon was drawn around it, of which the area was determined); average depth (Meter, the average depth of the lake was determined using depth measurements from 20 random points and 20 points along transects perpendicular to the longest axis of the lake); altitude (Meter above sea level, based on DEM from gtopo30, converted to an Arcview grid using the procedure published on herpnet.org GTOPT_DEM); conductivity (µS/cm); Total nitrogen (mg/L); Total phosphorus (mg/L); chlorophyll <i>a</i> (µg/L) -- TABLE: Please see in attached file. --
DOI: 10.6084/m9.figshare.3514541
title: Appendix A. Pollution-loading model: derivation/description of pollution-loading model used to quantify impacts of human land use patterns on in-stream salmon habitat productivity.

description: Pollution-loading model: derivation/description of pollution-loading model used to quantify impacts of human land use patterns on in-stream salmon habitat productivity.
DOI: 10.6084/m9.figshare.3514541.v1
title: Appendix A. Pollution-loading model: derivation/description of pollution-loading model used to quantify impacts of human land use patterns on in-stream salmon habitat productivity.

description: Pollution-loading model: derivation/description of pollution-loading model used to quantify impacts of human land use patterns on in-stream salmon habitat productivity.
DOI: 10.6084/m9.figshare.3514538
title: Appendix B. Model calibration: description of the model calibration routine, including translation of results from Pess et al. (2002) to loading factors for the pollution-loading model.

description: Model calibration: description of the model calibration routine, including translation of results from Pess et al. (2002) to loading factors for the pollution-loading model.
DOI: 10.6084/m9.figshare.3514538.v1
title: Appendix B. Model calibration: description of the model calibration routine, including translation of results from Pess et al. (2002) to loading factors for the pollution-loading model.

description: Model calibration: description of the model calibration routine, including translation of results from Pess et al. (2002) to loading factors for the pollution-loading model.
DOI: 10.6084/m9.figshare.3514535
title: Appendix C. Ricker results: table with parameter estimates and baseline persistence probabilities based on a Ricker stock–recruitment model, analagous to Table 1 based on a Beverton-Holt stock–recruitment model.

description: Ricker results: table with parameter estimates and baseline persistence probabilities based on a Ricker stock–recruitment model, analagous to Table 1 based on a Beverton-Holt stock–recruitment model.
DOI: 10.6084/m9.figshare.3514535.v1
title: Appendix C. Ricker results: table with parameter estimates and baseline persistence probabilities based on a Ricker stock–recruitment model, analagous to Table 1 based on a Beverton-Holt stock–recruitment model.

description: Ricker results: table with parameter estimates and baseline persistence probabilities based on a Ricker stock–recruitment model, analagous to Table 1 based on a Beverton-Holt stock–recruitment model.
DOI: 10.6084/m9.figshare.3514532
title: Appendix D. Increased cost variation: figures showing the effect of simulating greater variation in watershed protection costs, analagous to Fig. 5.

description: Increased cost variation: figures showing the effect of simulating greater variation in watershed protection costs, analagous to Fig. 5.
DOI: 10.6084/m9.figshare.3514532.v1
title: Appendix D. Increased cost variation: figures showing the effect of simulating greater variation in watershed protection costs, analagous to Fig. 5.

description: Increased cost variation: figures showing the effect of simulating greater variation in watershed protection costs, analagous to Fig. 5.
DOI: 10.6084/m9.figshare.3514529
title: Supplement 1. Matlab code to run the simulation/site-selection model and GAUSS code to estimate the stock–recruitment parameters, plus all input files.

description: File List SALMON.m Main MATLAB program—running this program with current parameter settings will reproduce all of the main results presented in the paper.<br> SALMON_BH_graphs.m MATLAB sub-program, called by SALMON.m<br> SALMON_concentrations.m MATLAB sub-program, called by SALMON.m<br> SALMON_ext_probs_fast.m MATLAB sub-program, called by SALMON.m<br> SALMON_forecast.m MATLAB sub-program, called by SALMON.m<br> SALMON_forecast_Ricker.m MATLAB sub-program, called by SALMON.m<br> SALMON_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_header.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_function.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_graph_Ricker.m MATLAB sub-program, called by SALMON.m<br> SALMON_raw_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_save.m MATLAB sub-program, called by SALMON.m<br> SALMON_set_parameters.m MATLAB sub-program, called by SALMON.m<br> SALMON_wild_graph.m MATLAB sub-program, called by SALMON.m<br> Entiat_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> Methow_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SalmonML.prg GAUSS code for estimating stock-recruitment parameters for each sub-stock<br> SalmonRawSpawners.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SalmonWildSpawners.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> WatershedAttributesAndRanks.txt description<br> WatershedAttributes.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> Wenatchee_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SALMONsupplement.zip All supplemental files at once Description To reproduce the main results given in the paper, copy all files in the list above to the same folder. Then use Matlab to run SALMON.m, from that folder, which will call the other Matlab subroutines (.m files) and input files (.txt files) as needed. Most of the input parameters can be found in SALMON_set_parameters.m, so that file can be modified to conduct further sensitivity analyses. To re-estimate the stock-recruitment parameters, use GAUSS to run SalmonML.prg.
DOI: 10.6084/m9.figshare.3514529.v1
title: Supplement 1. Matlab code to run the simulation/site-selection model and GAUSS code to estimate the stock–recruitment parameters, plus all input files.

description: File List SALMON.m Main MATLAB program—running this program with current parameter settings will reproduce all of the main results presented in the paper.<br> SALMON_BH_graphs.m MATLAB sub-program, called by SALMON.m<br> SALMON_concentrations.m MATLAB sub-program, called by SALMON.m<br> SALMON_ext_probs_fast.m MATLAB sub-program, called by SALMON.m<br> SALMON_forecast.m MATLAB sub-program, called by SALMON.m<br> SALMON_forecast_Ricker.m MATLAB sub-program, called by SALMON.m<br> SALMON_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_header.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_function.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_pva_graph_Ricker.m MATLAB sub-program, called by SALMON.m<br> SALMON_raw_graph.m MATLAB sub-program, called by SALMON.m<br> SALMON_save.m MATLAB sub-program, called by SALMON.m<br> SALMON_set_parameters.m MATLAB sub-program, called by SALMON.m<br> SALMON_wild_graph.m MATLAB sub-program, called by SALMON.m<br> Entiat_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> Methow_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SalmonML.prg GAUSS code for estimating stock-recruitment parameters for each sub-stock<br> SalmonRawSpawners.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SalmonWildSpawners.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> WatershedAttributesAndRanks.txt description<br> WatershedAttributes.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> Wenatchee_boot.txt Input file for MATLAB programs, called by SALMON_set_parameters.m<br> SALMONsupplement.zip All supplemental files at once Description To reproduce the main results given in the paper, copy all files in the list above to the same folder. Then use Matlab to run SALMON.m, from that folder, which will call the other Matlab subroutines (.m files) and input files (.txt files) as needed. Most of the input parameters can be found in SALMON_set_parameters.m, so that file can be modified to conduct further sensitivity analyses. To re-estimate the stock-recruitment parameters, use GAUSS to run SalmonML.prg.
DOI: 10.6084/m9.figshare.3514526
title: Appendix A. Additional temperature data for controls and open-topped chambers.

description: Additional temperature data for controls and open-topped chambers.
DOI: 10.6084/m9.figshare.3514526.v1
title: Appendix A. Additional temperature data for controls and open-topped chambers.

description: Additional temperature data for controls and open-topped chambers.
DOI: 10.6084/m9.figshare.3514523
title: Appendix B. Stem heights under different precipitation treatments.

description: Stem heights under different precipitation treatments.
DOI: 10.6084/m9.figshare.3514523.v1
title: Appendix B. Stem heights under different precipitation treatments.

description: Stem heights under different precipitation treatments.
DOI: 10.6084/m9.figshare.3514520
title: Appendix C. Pore water chemistry results.

description: Pore water chemistry results.
DOI: 10.6084/m9.figshare.3514520.v1
title: Appendix C. Pore water chemistry results.

description: Pore water chemistry results.
DOI: 10.6084/m9.figshare.3514517
title: Appendix A. Age models.

description: Age models.
DOI: 10.6084/m9.figshare.3514514
title: Appendix B. Sawgrass ridge pollen assemblages from core 02-05-21-2.

description: Sawgrass ridge pollen assemblages from core 02-05-21-2.
DOI: 10.6084/m9.figshare.3514511
title: Appendix C. Transition pollen assemblages from core 02-05-21-4.

description: Transition pollen assemblages from core 02-05-21-4.
DOI: 10.6084/m9.figshare.3514511.v1
title: Appendix C. Transition pollen assemblages from core 02-05-21-4.

description: Transition pollen assemblages from core 02-05-21-4.