In the recent geological past, Earth’s magnetic field reduced to 4% of the modern values and the magnetic poles moved severely apart from the geographic poles causing the Laschamps geomagnetic excursion, which happened about 41 millennia ago. The excursion lasted for about two millennia, with the peak strength reduction and dipole tilting lasting for a shorter period of 300 years. During this period, the geomagnetic field exhibited significant differences from the modern nearly-aligned dipolar field, causing non-dipole variables to mimic a magnetic field akin to the outer planets while displaying a significantly reduced magnetic strength. However, the precise magnetospheric configuration and their electrodynamic coupling with the atmosphere have remained critically understudied. This dataset contains the first space plasma investigation of the exact geomagnetic conditions in the near-Earth space environment during the excursion. The study contains a full 3D reconstruction and analysis of the geospace system including the intrinsic geomagnetic field, magnetospheric system and the upper atmosphere, linked in sequence using feedback channels for distinct temporal epochs. The reconstruction was conducted using the LSMOD.2 model, Block Adaptive Tree Solar wind-Roe-Upwind Scheme (BATS-R-US) Model and the MAGnetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model, all of which are publicly-available models.
The dataset contains the raw data from each of these models, in addition to the images/post-processing results generated using these models. Paleomagnetic data produced by LSMOD.2 can be visualized using a combination of linear plotting and contour plotting tools available commonly in visualization software like Python (e.g. Python/Matplotlib) or MATLAB. Standard tools to read and visualize BATS-R-US and MAGNIT output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html). For information and details about the post-processed data, visualization and analysis, please contact the authors for details. The anthropological dataset can be visualized using a shape file reader (e.g. Python/GeoPandas) and a linear plotting tool (e.g. Python/Matplotlib).
Inland lakes play a critical role in ecosystem stability, and robust validation of lake models is essential for understanding their dynamics. While remote sensing data can assist with lake surface temperature validation, in situ data typically provides more accurate, reliable data not limited to only the lake surface. However, in situ temperature data for many individual lakes, particularly in North America, is difficult for researchers to quickly access in a standardized format. This database offers a well-organized collection of in situ near-surface and subsurface temperatures from 134 sites divided among 29 large North American inland lakes collected from a variety of sources. The database includes multiple subsurface temperatures throughout the depth profile of 84 of these sites, providing comprehensive data for lake model evaluation. All lakes selected for this database are large enough (over approximately 30 km^2 to be represented by large-scale operational weather models, supporting robust lake model validation efforts on the lakes that have the greatest impact on climatology.
Sorensen, T., Espey, E., Kelley, J.G.W. et al. A database of in situ water temperatures for large inland lakes across the coterminous United States. Sci Data 11, 282 (2024). https://doi.org/10.1038/s41597-024-03103-8
We conducted a mixed-methods study to understand how financial incentive programs impact transitions to cover cropping in Michigan. Michigan farms span a wide range of soil types, climate conditions, and cropping systems that create opportunities for cover crop adoption in the state. We tested the relationship between Environmental Quality Incentives Program (EQIP) payments for cover crops and cover crop adoption between 2008-2019, as measured by remote sensing. Panel fixed effects regressions showed that EQIP increased winter cover crop presence. Every EQIP dollar for cover crops was associated with a 0.01 hectare increase in winter cover, while each hectare enrolled in an EQIP contract for cover crops was associated with a 0.86 – 0.93 hectare increase in winter cover.
Surdoval, A., Jain, M., Blair, E., Wang, H., and J. Blesh. In press. Financial incentive programs and farm diversification with cover crops: Assessing opportunities and challenges.
These files (.tiff format) provide high-resolution images of a fossil specimen complementing the formal description of the osteoglossid fish †Macroprosopon hiltoni from the Eocene (Ypresian) of Morocco.
Reconstructed CT slices for Snout region of Macroprosopon (catalog number FSAC CP 330; FSAC = Faculty of Sciences Aïn Chock, Casablanca, Morocco) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
Reconstructed CT slices for skull of Pantodon buchholzi (University of Michigan Museum of Zoology catalog number UMMZ Fish 249782) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
Reconstructed CT slices for skull of Hiodon tergisus (University of Michigan Museum of Zoology catalog number UMMZ Fish 247425) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
Reconstructed CT slices for whole specimen of Macroprosopon (catalog number FSAC CP 330; FSAC = Faculty of Sciences Aïn Chock, Casablanca, Morocco) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
Reconstructed CT slices for skull of Petrocephalus simus (University of Michigan Museum of Zoology catalog number UMMZ Fish 200167) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
Reconstructed CT slices for skull of Chitala blanci (University of Michigan Museum of Zoology catalog number UMMZ Fish 232272) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
