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1. Rain stable water isotope data from a network of stations around Peru during 2024 wet season

   https://doi.org/10.4231/83s5-6t75  

   Welp, Lisa; Romero, Carol; Valdivia, Adriana; Michalski, Greg; Reyes_Larico, Juan Amilcar; Valenzuela_Huillca, Carlos Javier; Soto_Catasi, Silvia Milagros; Mamani_Chuctaya, Dayam Mitchell; Pfoccoalata_Montañez, Sandra Olinda; Salas_Yaulli, Andrea_Maria Fernanda; et al (December 2026, Purdue University Research Repository)

   {"Abstract":["<p>This data repository was produced with support from NSF award #<span style="font-size:11.0pt"><span style="line-height:115%"><span aptos="" style="font-family:">2403869</span></span></span>&nbsp; &quot;<b><span style="font-size:11.0pt"><span style="line-height:115%"><span aptos="" style="font-family:">RAPID: Measuring the Isotopic Fingerprint of the South American Summer Monsoon during a Strong El Nino, 2023-2024</span></span></span>&quot;</b>.</p>\n\n<p>Coastal Peru receives generally low amounts of precipitation typically transported by strong easterly winds at high altitudes from the Amazon basin over the Andes, but at times from Pacific moisture transported onshore. Here, we examined daily rain amounts and oxygen and hydrogen isotopic ratios from a network of collectors in the southern Peruvian district of Arequipa on the western flank of the Andes and extending along the coast to the north during the 2024 summer wet season (December 2023 &ndash; March 2024). During this time, the Pacific experienced the 5th strongest El Ni&ntilde;o event on record. However, northern Peru did not receive extreme rainfall as it sometimes does in strong El Ni&ntilde;o years.&nbsp;</p>"],"Other":["We analyzed daily rainfall across Peru (but concentrated around Arequipa) during the 2024 wet season for water stable isotopes to study sub-seasonal precipitation dynamics including time-varying moisture source influence during a strong El Niño year."]} 

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2. Luquillo LTER signature Monthly and Daily dataset

   https://doi.org/10.6073/pasta/15caa75d4e779eeb3a05e38c2c7d342e  

   Leon, Miguel C (October 2024, Environmental Data Initiative)

   This dataset contains population, meteorological, phenological, and chemical data collected from tropical montane stream and forest ecosystems. This synthesis product merges 11 distinct datasets into a single comprehensive resource, facilitating easier comparison and study of diverse ecological data. Try our data explorer [here.](https://luqshiny.lter.network/sigds/) It provides a multi-faceted dataset essential to enhance biodiversity monitoring, ecosystem management, and global change research. Data spans from 1975 to 2024, with some gaps and missing data points. Particularly at the beginning of the time span measurements started with temperature and rainfall before other dataset collections began with the inception of the LUQ LTER in the late 1980s. For phenology 17 species were selected for inclusion, listed below. 11 dominant species as described in Uriarte et al. 2009 (DOI 10.1890/08-0707.1). As well as in decline species PALRIP- Palicourea riparia and CISVER - Cissus verticillata. Species that show a strong response to hurricanes PHYRIV - Phytolacca rivinoides- an understory herb and IPOTL -Ipomoea tiliacea- Morning glory vine. And a species thriving understory shrub SMIDOM - Smilax domingensis. - **From Uriarte et al. 2009 these 11 species represent 75% of the stems >=10 cm dbh in the LFDP plot: ** - **ALCLAT** - Alchornea latifolia - **CASARB** - Casearia arborea - **CECSCH** - Cecropia schreberiana - **DACEXC** - Dacryodes excelsa - **GUAGUI** - Guarea guidonia - **INGLAU** - Inga laurina - **MANBID** - Manilkara bidentata - **PREMON** - Prestoea montana - **SCHMOR** - Schefflera morototoni - **SLOBER** - Sloanea berteriana - **TABHET** - Tabebuia heterophylla - **PALRIP** - Palicourea riparia - For more details on methods and variables see each individual dataset: - Canopy Trimming Experiment (CTE) Litterfall: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=162](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=162) - Litterfall in tabonuco (subtropical wet) forest in the Luquillo Experimental Forest, Puerto Rico (MRCE Litterfall data): [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=111](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=111) - Rainfall at El Verde Field Station, Rio Grande, Puerto Rico since 1975: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=14](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=14) - Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico since October 1992: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=16](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=16) - Minimum temperature at El Verde Field Station, Rio Grande, Puerto Rico since 1975: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=17](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=17) - Chemistry of stream water from the Luquillo Mountains, Quebrada Sonadora and Quebrada Prieta: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=20](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=20) - Shrimp populations in Quebrada Prieta (Pools 0, 8, 9, 15) (El Verde): [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=54](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=54) - Meteorological data from El Verde Field Station: NADP Tower: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=127](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=127) - Chemistry of rainfall and throughfall from El Verde and Bisley: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=174](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=174) - Phenologies of the Tabonuco Forest trees and shrubs: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=88](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=88) - Prieta streams - Discharge and water level: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=182](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=182) We would like to acknowledge the contributions of all those who have worked in the Luquillo LTER over the course of its 36 year and counting history. The LUQ LTER thanks you for your contribution. Code for generating this signature dataset are available on github here: [https://github.com/miguelcleon/LUQ-LTER-signature-dataset](https://github.com/miguelcleon/LUQ-LTER-signature-dataset) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support was provided by the USDA Forest Service International Institute of Tropical Forestry and the University of Puerto Rico. 

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3. "Cohorts of immature Pteropus bats show interannual variation in Hendra virus serology

   https://doi.org/10.5281/zenodo.15982444  

   ymous, anon (January 2025, Zenodo)

   {"Abstract":[" LUMINEX Wildlife Disease Analysis Pipeline\n\n  Overview\n\n  Bayesian analysis pipeline for "Cohorts of immature Pteropus bats show interannual variation in Hendra virus serology"\n\nSummary \n\n  Prerequisites\n\n  Software Requirements\n\n  - R (≥ 4.0.0)  - Stan (≥ 2.21)\n\n  Required R Packages\n\n  # Core packages  install.packages(c("rstan", "tidyverse", "here", "loo", "bayesplot"))  # Additional packages    install.packages(c("RColorBrewer", "cowplot", "boot", "see", "factoextra", "bestNormalize", "LaplacesDemon", "ggpubr", "plyr", "see", "pscl"))    # Core utility functions (CRITICAL DEPENDENCY)  source(here("R", "useful_functions.R"))  # Bayesian analysis functions  source(here("R", "paper_theme.R"))      # Plotting themes    Hardware Requirements\n\n  - Storage: ≥5GB free space  - CPU: Multi-core processor recommended\n\n  Data Requirements\n\n  /raw_sharable folder\n\n  Execution Workflow\n\n  ⚠️ CRITICAL: Execute in This Order\n\n  Phase 1: Core Data Processing\n\n  1. Initial Data Cleaning  source("R/create_datasets_for_stan_part_1.R")    - Runtime: ~5-10 minutes    - Outputs: data_for_cohort_model.csv, luminex_igg_igm.csv  2. Serology Classification  rmarkdown::render("R/mixture_model_final.R")    - Runtime: ~30-60 minutes (Stan model fitting)    - Outputs: serology_prob.csv  3. Age/Cohort Assignment  rmarkdown::render("R/cohort_model_2025_05_12.Rmd")    - Runtime: ~1-3 hours (complex Stan models)    - Outputs: age_predictions.csv & a species comparison analysis   4. Dataset Integration  source("R/create_datasets_for_stan_part_2_06_30_24.R")    - Runtime: ~10-15 minutes    - Outputs: All analysis-ready datasets with time-alive variables\n\n  Phase 2: Primary Analyses\n\n  5. Prevalence Smoothing Analysis  rmarkdown::render("R/gaussian_smooth_prevalence_06_30_24.Rmd")    - Runtime: ~10+ hours (multiple Stan models)    - Key outputs: Prevalence curves, model comparisons    - Additional outputs:       -Basic prevalence smoothing (4 pathogens × 4 cohorts)      -Site-specific analysis (8-cohort models)      -Sex-stratified analysis      -Stringent cohort cutoff analysis      -Multiple cutoff threshold testing (5 different cutoffs)      -Date-based modeling      -Batch effect testing      -Adult vs juvenile comparisons  6. Logistic Regression Analysis  source("R/logistic_models_fig_3.R")  source("R/logistic_models_fig_2.R")    - Runtime: ~30-60 minutes    - Outputs: Figure 2 & 3 plots\n\n  Phase 3: Supporting Analyses (Optional)\n\n  7. Additional Analyses (run as needed):    - adult_prevalence_curves.Rmd - Adult dynamics    - PCA_new_analysis.R - Multivariate analysis    - additional_figures.R - Supplementary figures\n\n  Key Functions (useful_functions.R)\n\n  - fit_4cohort_model() - Bayesian 4-cohort prevalence model  - compile_stan_results() - Extract and format Stan results  - create_time_sequence() - Generate prediction timepoints  - compute_loo_cv() - Leave-one-out cross-validation  - plot_parameter_diagnostics() - Model diagnostic plots\n\n  Troubleshooting\n\n  Common Issues\n\n  Stan compilation errors:  # Recompile Stan models  rstan_options(auto_write = TRUE)  options(mc.cores = parallel::detectCores())\n\n  Memory issues:  - Reduce Stan iterations: ITER = 1000 instead of ITER = 2000  - Run analyses sequentially, not in parallel\n\n  Missing dependencies:  # Load all utility functions  source(here("R", "useful_functions.R"))  source(here("R", "paper_theme.R"))\n\n  Resume from Saved Results\n\n  Many scripts save intermediate results:  # Check for existing model fits  if(file.exists("model_results.RData")) {    load("model_results.RData")  } else {    # Run full analysis  }\n\n  Output Structure\n\n  Luminex_figs/r_figs/     # All generated figures  Data_for_publication/    # Final analysis datasets  stan/                    # Stan model files  R/                       # Analysis scripts\n\n  Expected Runtime\n\n  Full pipeline: 10-20 hours on modern hardware  \n\n \n\n "]} 

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4. Replication Data for: Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations

   https://doi.org/10.7910/dvn/ospldg  

   Collaboration, IceCube; Collaboration, IceCube (January 2025, Harvard Dataverse)

   {"Abstract":["Data and plotting scripts for reproducing plots from "Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations". Please refer to the related publication for a detailed explanation of the sample and analysis.\n<br><br>\nContents: \n<ol>\n<li> <code>README.md</code> contains useful information about the contents of this data release.</li>\n<li> <code>example.py</code> Example script demonstrating how to load the csv files, plot the chi squared map, and extract the 90% sensitivity contours shown in Figures 11 and 15.</li>\n<li> <code>modchi2map_nufitwoSK.csv</code> chi2 map used to produce Figure 11 (left), in which the injected truth point is the best fit point from <a href="http://www.nu-fit.org/sites/default/files/v52.tbl-parameters.pdf">NuFit 5.2 w/o SK (upper panel)</a>, sin²(&theta;₂₃)=0.572 and &Delta;m²₃₂=2.43&times;10^-3 eV².</li>\n<li> <code>modchi2map_nufitwSK.csv</code> chi2 map used to produce Figure 11 (right), in which the injected truth point is the best fit point from <a href="http://www.nu-fit.org/sites/default/files/v52.tbl-parameters.pdf">NuFit 5.2 w/ SK (lower panel)</a>, sin²(&theta;₂₃)=0.451 and &Delta;m²₃₂=2.43&times;10^-3 eV².</li>\n<li> <code>modchi2map_icecube.csv</code> chi2 map used to produce Figure 15, in which the injected truth point is the best fit point from the latest published IceCube DeepCore oscillation result <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.091801">Phys. Rev. Lett. 134, 091801 (2025)</a>, sin²(&theta;₂₃)=0.54 and &Delta;m²₃₂=2.40&times;10^-3 eV².</li>\n</ol>\n\nPlease note: The CSV files are available for download through dataverse in either csv or tab format."]} 

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5. A data directory to facilitate investigations on worldwide wildlife trafficking

   https://doi.org/10.1080/20964471.2023.2193281  

   Gore, Meredith L.; Hilend, Rowan; Prell, Jonathan O.; Griffin, Emily; Macdonald, John R.; Keskin, Burcu B.; Ferber, Aaron; Dilkina, Bistra (March 2023, Big Earth Data)

   Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is signifi-cantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer- reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data direc-tory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data- driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network inter-diction problems. 

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