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- Creator:
- Heath, Jeffrey
- Description:
- these and other recordings are data for a reference grammar of Kelenga that, when completed, will be archived in the collection "Bozo languages of Mali (documents)" in Deep Blue Documents. For contents see the "notes" file inside the work. A few of the Kelenga texts are being transcribed, others will be left for others to transcribe or listen to as they wish.
- Keyword:
- Bozo and Kelenga
- Discipline:
- Humanities
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- Creator:
- Heath, Jeffrey
- Description:
- recordings made in Barato village. Referred to as "text 2021-02" and "text 2021-03." Text 2021-03 is transcribed and annotated at the end of the reference grammar (see link to Deep Blue Documents). Text 2021-02 covers a subset of the same content and has not been transcribed as of late 2022. See also "notes" file inside the work.
- Keyword:
- Bozo, Jenaama, Sorogaama
- Discipline:
- Humanities
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- Creator:
- Heath, Jeffrey
- Description:
- recording in mp3 format. The reference grammar (see link to Deep Blue Documents) presents transcription and analysis as "text 2021-01."
- Keyword:
- Bozo, Jenaama, Sorogaama
- Discipline:
- Humanities
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- Creator:
- Heath, Jeffrey
- Description:
- For content see the "notes" file inside the work. Most of the recordings are translated and annotated at the end of the reference grammar (see link to Deep Blue Documents).
- Keyword:
- Bozo, Jenaama, Cliffs
- Discipline:
- Humanities
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- Creator:
- Butterfield, Zachary, Muccio, Daniel, and Keppel-Aleks, Gretchen
- Description:
- Solar-induced chlorophyll fluorescence (SIF) is an emission of photons during photosynthesis that can be used to make inferences about gross primary productivity (GPP) and carbon uptake of vegetation. With a recent proliferation of available satellite-based observations of SIF, there is much interest in assessing how SIF relates to GPP across multiple temporal and spatial scales. Tower-based observations of SIF at high temporal resolution provide a key link between satellite data and local surface-based observations of ecosystem productivity. We collected tower-based observations of SIF and several vegetation indices using a PhotoSpec spectrometer system deployed on the AmeriFlux tower at UMBS (US-UMB). As the data were collected alongside concurrent eddy flux observations of carbon exchange, they provide a unique opportunity to explore how SIF and other vegetation signals relate to GPP in a temperate deciduous forest and better inform the interpretation of satellite observations.
- Keyword:
- Solar-induced chlorophyll fluorescence, gross primary production, temperate deciduous forest, remote sensing, flux observations, forest productivity
- Citation to related publication:
- Butterfield, Z., Magney, T., Grossmann, K., Bohrer, G., Vogel, C., Barr, S., & Keppel-Aleks, G. (2023). Accounting for Changes in Radiation Improves the Ability of SIF to Track Water Stress-Induced Losses in Summer GPP in a Temperate Deciduous Forest. Journal of Geophysical Research: Biogeosciences, 128, e2022JG007352. https://doi.org/10.1029/2022JG007352
- Discipline:
- Science
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- Creator:
- Yu, Chi-Lin, Eggleston, Rachel, Zhang, Kehui, Nickerson, Nia, Sun, Xin, Marks, Rebecca A., Hu, Xiaosu, Brennan, Jonathan R. , Wellman, Henry M. , and Kovelman, Ioulia
- Description:
- The dataset includes 51 children (age range = 6-12 years) who listened to the first chapter of Alice’s Adventures in Wonderland during fNIRS neuroimaging. We also provide the text of the story with several word-by-word predictors motivated by research in Theory of Mind development and language. These annotated, naturalistic datasets can be used to replicate prior work and test new hypotheses about everyday social cognition and natural language comprehension in the developing brain.
- Keyword:
- neuroimaging, fNIRS, Children, Theory of Mind, Language, Naturalistic Neuroscience, and Story Listening
- Discipline:
- Science
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- Creator:
- Kort, Eric A., Plant, Genevieve, and Dacic, Natasha
- Description:
- As part of the Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) project, in 2021 the aircraft platform sampled the agriculture regions of Nebraska and Iowa. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data files contains the merged data for each individual file day.
- Keyword:
- Greenhouse Gas, Agriculture , and Nitrous Oxide
- Citation to related publication:
- Gvakharia A, Kort EA, Smith M, Conley S, Testing and evaluation of a new airborne system for continuous N2O, CO2, CO, and H2O measurements: the Frequent Calibration High-performance Airborne Observation System (FCHAOS), Atmos. Meas. Tech. 11, 6059-6074, https://doi.org/10.5194/amt-11-6059-2018, 2018, Conley S, Faloona I.C, Lenschow D.H, Karion A, Sweeney S, (2014) A low-cost system for measuring horizontal winds from single-engine aircraft, Journal of Atmospheric and Oceanic Technology, 31(6), 1312-1320, https://doi.org/10.1175/JTECH-D-13-00143.1, Airborne measurements reveal high spatiotemporal variation and the heavy-tail characteristic of nitrous oxide emissions in Iowa" by Natasha Dacic, Genevieve Plant, and Eric A Kort. Journal of Geophysical Research: Atmospheres. Submitted., and 2022 dataset: Kort, E. A., Plant, G., Dacic, N. (2024). Aircraft Data (2022) for Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/tmfd-nw87
- Discipline:
- Science
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Bozo languages of Mali (spreadsheets, media)
User Collection- Creator:
- Heath, Jeffrey G.
- Description:
- This collection will archive lexical spreadsheets, audio files, geographic information, images, and videos that complement the reference grammars in pdf and docx form in the collection “Bozo languages of Mali (documents)” that are archived in Deep Blue Documents ( https://dx.doi.org/10.7302/6632). See the “readme” for that collection and the introductory material in the reference grammars for general information about the languages and the fieldwork., The initial material archived in the present collection consists of audio files. They are recordings of narratives, interviews, and conversations. Some of them have been transcribed and are presented as appendices in the reference grammars. Others have not been transcribed; they are presented here in the hope that they can eventually be transcribed or at least listened to by native speakers. If the author is able to transcribe some of them in the future, the transcriptions will be added here (and to the Deep Blue Documents collection)., Many of the recordings, as well as most of the images and videos to be added to this collection, have been made by project assistant Minkailou Djiguiba. He has courageously traveled into Bozo-speaking zones, some of which are highly insecure, to do this work. In addition, he has been instrumental in recruiting and transporting Bozo speakers to the author’s base in Bobo Dioulasso where much of the grammatical and lexical work has been done., and The author’s fieldwork is supported by grant PD-1941828 (2020-2024) from the National Science Foundation, Documenting Endangered Languages program, which is also supported by the National Endowment for the Humanities.
- Keyword:
- Bozo, Jenaama, Cliffs, Kelenga, Tigemaxo, and Tiéyaxo
- Discipline:
- Humanities
5Works -
- Creator:
- Hernandez, Isabel, Sun, Xin, Zhang, Kehui, Marks, Rebecca, Karas, Zachary, Eggleston, Rachel, Nickerson, Nia, Yu, Chi-Lin, Wagley, Neelima, Hu, Xiaosu, Caruso, Valeria, Tardif, Twila, Satterfield, Teresa, Chou, Tai-Li, and Kovelman, Ioulia
- Description:
- In a broad sense, this dataset explores morphological and phonological processing in English monolinguals and two bilingual populations, Chinese-English and Spanish-English, using a battery of standardized and self-developed behavioral measures. Language: English - Spanish - Chinese
- Discipline:
- Science
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Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles
- Creator:
- Saldinger, Jacob, Raymond, Matt , Elvati, Paolo, and Violi, Angela
- Description:
- The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at https://software.rcc.uchicago.edu/mscg/
- Keyword:
- Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
- Citation to related publication:
- https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
- Discipline:
- Science