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- Creator:
- Agnit Mukhopadhyay, Sanja Panovska, Raven Garvey, Michael Liemohn, Natalia Ganjushkina, Austin Brenner, Ilya Usoskin, Michael Balikhin, and Daniel Welling
- Description:
- 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).
- Discipline:
- Engineering and Science
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- Creator:
- Surdoval, Alison, Jain, Meha, Wang, Haoyu, and Blesh, Jennifer
- Description:
- 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.
- Keyword:
- adoption, cover crop, Environmental Quality Incentives Program, financial incentive program, Michigan, remote sensing
- Citation to related publication:
- 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.
- Discipline:
- Science
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- Creator:
- Capobianco, Alessio
- Description:
- 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.
- Keyword:
- Zoology, Ichthyology, Fish, CT, Mormyridae, and UMMZ
- Discipline:
- Science
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- Creator:
- CTEES
- Description:
- 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.
- Keyword:
- Zoology, Ichthyology, Fish, CT, Pantodontidae, and UMMZ
- Discipline:
- Science
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- Creator:
- CTEES
- Description:
- 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.
- Keyword:
- Zoology, Ichthyology, Fish, CT, Hiodontidae, and UMMZ
- Discipline:
- Science
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- Creator:
- CTEES
- Description:
- Reconstructed CT slices for skull of Heterotis niloticus (University of Michigan Museum of Zoology catalog number UMMZ Fish 195004) 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.
- Keyword:
- Zoology, Ichthyology, Fish, CT, Osteoglossidae, and UMMZ
- Discipline:
- Social Sciences
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- Creator:
- Capobianco, Alessio
- Description:
- 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.
- Keyword:
- Zoology, Ichthyology, Fish, CT, Notopteridae, and UMMZ
- Discipline:
- Science
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- Creator:
- Dariya, Malyarenko, Tariq, Humera, Kushwaha, Aman, Mourad, Rami, Heist, Kevin, Chenevert, Thomas L, Ross, Brian D, Chen, Heang-Ping, and Hadjiiski, Lubomir
- Description:
- The 3D GRE MRI data for murine model of myelofbifrosis with expert segmentations of mouse tibia was used to train Attention UNET model to automate bone marrow segmentation for measurements of imaging biomarkers. This dataset consists of three archives: (1) containing the source MRI images in Meta-image-header (MHD) format with resulting segmentation labels by two experts and four UNET models with different training scenarios; (2) corresponding training models; and (3) deep-learning (DL)-based segmentation tools for application to future murine tibia MRI data. and The MHD images are an ITK compatible format that can be viewed in standard image viewer, like 3D Slicer. The image archive is structured with a directory tree that contains \"mouseID"\"scan-date"\"segmentaion-scenario"\. The "training model" archive containes DL-model labeled by the data subset, and "deployment" archive containes the DL-segmentation software.
- Keyword:
- deep-learning segmentation, preclinical MRI, murine tibia, and mouse model of myelofibrosis
- Citation to related publication:
- Kushwaha A, Mourad RF, Heist K, Tariq H, Chan HP, Ross BD, Chenevert TL, Malyarenko D, Hadjiiski LM. Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning. Tomography. 2023 Mar 7;9(2):589-602. doi: 10.3390/tomography9020048. PMID: 36961007; PMCID: PMC10037585. and https://github.com/dumichgh/MFJK1_Segmentation_MHDs
- Discipline:
- Health Sciences
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- Creator:
- Malyarenko, Dariya, Chenevert, Thomas L, Heist, Kevin, Bonham, Christopher, and Ross, Brian
- Description:
- The imaging data was used to measure repeatability and temporal trends of quantitative imaging biomarkers of myelofibrosis in bone marrow based on apparent diffusion coefficient, fat fraction and magnetization transfer ratio. The dataset consists of time series of the MRI Meta-image-header (MHD) images of wild type and diseased mice combined by the imaging time point. The MHD images are an ITK compatible format that can be viewed in standard image viewer, like 3D Slicer. Each time point image archive is structured with a directory tree that contains ./././"mouseID"/"scan-date"/"acquisition type"/
- Keyword:
- murine tibia MRI, bone marrow imaging, apparent diffusion coefficient (ADC), proton density fat fraction (PDFF), magnetization transfer ratio (MTR), and pre-clinical model of myelofibrosis
- Citation to related publication:
- Ross BD, Malyarenko D, Heist K, Amouzandeh G, Jang Y, Bonham CA, Amirfazli C, Luker GD, Chenevert TL. Repeatability of Quantitative Magnetic Resonance Imaging Biomarkers in the Tibia Bone Marrow of a Murine Myelofibrosis Model. Tomography. 2023 Feb 28;9(2):552-566. doi: 10.3390/tomography9020045. PMID: 36961004; PMCID: PMC10037563.
- Discipline:
- Health Sciences
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- Creator:
- Bautista-Arredondo, Luis F., Muñoz-Rocha, T. Verenice, Figueroa, José L., Téllez-Rojo, Martha M., Torres-Olascoaga, Libni A., Cantoral, Alejandra, Arboleda-Merino, Laura C., Leung, Cindy, Peterson, Karen E., and Lamadrid-Figueroa, Héctor
- Description:
- Data was collected from participants of the Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) study, which consists of three sequentially-enrolled birth cohorts of pregnant women. Research protocols of this study were approved by the Institutional Review Board at University of Michigan and the Mexico National Institute of Public Health. We obtained informed consent from study participants prior to enrollment.
- Keyword:
- Food Insecurity, COVID-19 Pandemic, Mexico, Cohort
- Citation to related publication:
- Bautista-Arredondo LF, Verenice Muñoz-Rocha T, Figueroa JL, Téllez-Rojo MM, Torres-Olascoaga LA, Cantoral A, Arboleda-Merino L, Leung L, Peterson KE, and Lamadrid-Figueroa H. A surge in food insecurity during the COVID-19 pandemic in a cohort in Mexico City. 2022. Article in process of publication.
- Discipline:
- Health Sciences