cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2025-01.3368508/2508406
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368603/2508407
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368623/2508408
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368759/2508409
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368769/2508410
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368780/2508411
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368790/2508412
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3368801/2508413
title: Data for EMSL Project 60923 from January 2025
DOI: 10.25582/data.2025-01.3364226/2508414
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364238/2508415
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364245/2508416
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364248/2508417
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364253/2508418
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364273/2508419
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364281/2508420
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364296/2508421
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364304/2508422
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364385/2508423
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364399/2508424
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364444/2508425
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364530/2508426
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364555/2508427
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364577/2508428
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364599/2508429
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364632/2508430
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364692/2508431
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364754/2508432
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364779/2508433
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364877/2508434
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364931/2508435
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365104/2508436
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365126/2508437
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365142/2508438
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365173/2508439
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365190/2508440
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365222/2508441
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365233/2508442
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365249/2508443
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365257/2508444
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365278/2508445
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365303/2508446
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365325/2508447
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365335/2508448
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365343/2508449
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365365/2508450
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365409/2508451
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365416/2508452
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365421/2508453
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365445/2508454
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365478/2508455
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365489/2508456
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365499/2508457
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365511/2508458
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365520/2508459
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365525/2508460
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365552/2508461
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365564/2508462
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365575/2508463
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365589/2508464
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365606/2508465
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365625/2508466
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365721/2508467
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365739/2508468
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365766/2508469
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365874/2508470
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365905/2508471
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365937/2508472
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365954/2508473
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3365988/2508474
title: Data for EMSL Project 60886 from January 2025
DOI: 10.25582/data.2025-01.3364529/2508475
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366253/2508476
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366363/2508477
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366392/2508478
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366408/2508479
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366426/2508480
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366513/2508481
title: Data for EMSL Project 60933 from January 2025
DOI: 10.15468/dl.ejx8ue
title: Occurrence Download

description: A dataset containing 1496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Accipiter bicolor (Vieillot, 1817)"
 ]
}

 The dataset includes 1496 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007578-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2025-01.3366547/2508482
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366685/2508483
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366819/2508484
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3366987/2508485
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367033/2508486
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367081/2508487
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367166/2508488
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367189/2508489
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367244/2508490
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367267/2508491
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367279/2508492
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367311/2508493
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367370/2508494
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367395/2508495
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367441/2508496
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367456/2508497
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367741/2508498
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367816/2508499
title: Data for EMSL Project 60933 from January 2025
DOI: 10.5281/zenodo.14080379
title: Gubra LSFM header/label tweaks

description: This contains a subset of the data from the https://github.com/Gubra-ApS/LSFM-mouse-brain-atlas repository (associated paper: https://pubmed.ncbi.nlm.nih.gov/33063286/), making minor tweaks to set the voxel spacing correctly in the niftis, a rigid reorientation to the ABAv3 space, and re-mapping of labels to the ABAv3 label numbers.
DOI: 10.5281/zenodo.14080380
title: Gubra LSFM header/label tweaks

description: This contains a subset of the data from the https://github.com/Gubra-ApS/LSFM-mouse-brain-atlas repository (associated paper: https://pubmed.ncbi.nlm.nih.gov/33063286/), making minor tweaks to set the voxel spacing correctly in the niftis, a rigid reorientation to the ABAv3 space, and re-mapping of labels to the ABAv3 label numbers.
DOI: 10.25582/data.2025-01.3367838/2508500
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367856/2508501
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367885/2508502
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367904/2508503
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3367994/2508504
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368051/2508505
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368063/2508506
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368092/2508507
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368162/2508508
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368169/2508509
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368194/2508510
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368206/2508511
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368216/2508512
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368233/2508513
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368274/2508514
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368528/2508515
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368583/2508516
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368632/2508517
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368652/2508518
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368672/2508519
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-01.3368739/2508520
title: Data for EMSL Project 60933 from January 2025
DOI: 10.25582/data.2025-02.3368876/2508521
title: Data for EMSL Project 60933 from February 2025
DOI: 10.25582/data.2025-02.3368895/2508522
title: Data for EMSL Project 60933 from February 2025
DOI: 10.15468/dl.m6a9bw
title: Occurrence Download

description: A dataset containing 30024 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Centaurea stoebe L., Reynoutria japonica Houtt., Heracleum mantegazzianum Sommier & Levier, Impatiens glandulifera Royle, Lythrum salicaria L., Vincetoxicum rossicum (Kleopow) Barbar.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Canada",
 "Year is one of (1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024)"
 ]
}

 The dataset includes 30024 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007579-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2025-02.3368906/2508523
title: Data for EMSL Project 60933 from February 2025
DOI: 10.5066/p9l6trzw
title: Statistical and groundwater-flow models of the Fountain Creek alluvial aquifer near Colorado Springs, Colorado

description: The U.S. Geological Survey (USGS), in cooperation with the Air Force Civil Engineering Center, created a numerical groundwater-flow model for the Fountain Creek alluvial aquifer using the finite-difference MODFLOW code with the Newton formulation solver. This numerical groundwater-flow model simulates water-budget components, groundwater-flow directions, and groundwater-flow paths of the Fountain Creek alluvial aquifer. The numerical model was spatially discretized into a single layer with 291 rows and 254 columns of 200 by 200 feet size, and a total of 17,610 active cell. The numerical model was temporally discretized into 1 initial steady-state stress period representing the average conditions of the 240 monthly transient stress periods from 2000 to 2019. This USGS data release includes all the necessary files to simulate the Fountain Creek alluvial aquifer and potential flow paths within it as described in the associated USGS Scientific Investigations Report (https://doi.org/10.3133/sir20235119).
DOI: 10.5066/p1hzes2r
title: Estuarine Shoreline, Upland Boundary, and Marsh Habitat Change Analyses for the Point Aux Chenes and Grand Bay Estuary Systems, Mississippi and Alabama

description: Coastal marshes across the Northern Gulf of Mexico are critical ecosystems, providing wildlife habitat, nursery for many species of fish, a carbon sink, and protection of communities from storm surge, but are vulnerable to coastal hazards, such as wave attack, sea level rise, and subsidence. While many marshes accrete vertically to compensate for rising sea level, marsh loss can occur through shoreline exposure to waves and lateral shoreline erosion. However, marsh migration (marsh transgression into upland habitats) may compensate for marsh shoreline erosion. Past marsh upland boundary and shoreline change trends are crucial data for understanding the processes driving erosion and migration and evaluating the overall health and vulnerability of coastal marshes to future scenarios of sea-level rise, climate change, and the global carbon budget.

The data presented in this data release include 1) 1848-2023 shoreline change analysis (vectorized marsh shorelines, shorepoints, and transects with shoreline change rates), 2) 1848-2022 upland boundary change analysis (vectorized upland boundary lines, boundary points, and transects with boundary change rates), 3) raster data for marsh habitat change analysis (1848, 1957/58, and 2019/22 rasters as well as 1848-1957/58, 1848-2019/22, and 1957/58-2019/22 change rasters), and 4) waves, fetch, and associated shoreline change rates for the Point Aux Chenes and Grand Bay estuary systems in Mississippi and Alabama, including the Grand Bay National Estuarine Research Reserve and Grand Bay National Wildlife Refuge.
DOI: 10.5066/p986zr6b
title: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output

description: The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment concentrations, sediment fluxes, initial and final bed levels, and hydrodynamic data are included in this dataset. Model output from six scenarios (comprised of three restoration alternatives and two sea level conditions) as forced by 18 climatological wind and wave forcing conditions are included. The Delft3D was used to assess effects of interior headland restoration. Appropriate use of this data includes the assessment of morphological, sediment process, and hydrodynamic differences due to the placement of restored features and sea level for the Grand Bay estuary. Data presented includes model grid coordinates, input elevations with and without restoration features, suspended sediment concentrations, sediment fluxes, morphologic data, and hydrodynamic variables of significant wave heights and water levels. For further information regarding data collection and/or processing methods, refer to the associated journal article (Jenkins and others, 2023). Jenkins, R.L., Passeri, D.P., Smith, C.G., Thompson, D.M., and Smith, K.E.L., 2023, Modeling the effects of interior headland restoration on estuarine sediment transport processes in a marine-dominant estuary: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2023.1217830.
DOI: 10.15468/dl.74sunc
title: Occurrence Download

description: A dataset containing 380426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2024-12-07",
 "EventDate is less than or equal to 2025-01-06",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 380426 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007577-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epxcfz
title: Occurrence Download

description: A dataset containing 64134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-01-07",
 "EventDate is less than or equal to 2025-02-06",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 64134 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007580-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14788356
title: Advancing Real-time Infectious Disease Forecasting Using Large Language Models

description: Forecasting the short-term spread of an ongoing disease outbreak poses a challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables, and the intersection of public policy and human behavior. Our work introduces PandemicLLM, a framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information. This approach, through an AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is tested across all 50 states of the U.S. for a duration of 19 months. PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models.
DOI: 10.5281/zenodo.14788357
title: Advancing Real-time Infectious Disease Forecasting Using Large Language Models

description: Forecasting the short-term spread of an ongoing disease outbreak poses a challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables, and the intersection of public policy and human behavior. Our work introduces PandemicLLM, a framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information. This approach, through an AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is tested across all 50 states of the U.S. for a duration of 19 months. PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models.