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1st Millennium BCE
User Collection
- Creator:
- Vani Archaeological Survey
- Description:
- 1st millennium BCE activity documented by the Vani Archaeological Survey
- Keyword:
- 1st Millennium BCE
11Works -
16th-17th cent. CE
User Collection
- Creator:
- Vani Archaeological Survey
- Description:
- 16th-17th cent. CE activity documented by the Vani Archaeological Survey
- Keyword:
- 16th-17th cent. CE
7Works -
- Creator:
- Bradshaw, Lisa, Vernon, Julianne, Schmidt, Thomas, James, Timothy, Zhang, Jianzhi, Archbold, Hilary, Cadigan, Ken, Wolfe, John P., and Goldberg, Deborah E.
- Description:
- This is the experimental data referenced in our manuscript entitled "Influence of CUREs on STEM retention depends on demographic identities." The dataset comprises csv files with results from student surveys given to students enrolled in Biology 173 from Fall 2015 through Fall 2019 as well as institutional data of their course grades and cumulative GPA at the time they enrolled in Biology 173, and graduation and major data for student who had graduated by 2021. The survey questions used in the analysis and the IRB consent form are also included as pdfs.
- Keyword:
- undergraduate research, STEM retention, CURE, introductory biology laboratory, and education research
- Citation to related publication:
- Bradshaw, L., Vernon J., Schmidt T., James T., Zhang J., Archbold H., Cadigan K., Wolfe J.P. & Goldberg D. 2023. Research article: Influence of CUREs on STEM retention depends on demographic identities. J Microbiol Biol Educ (accepted)
- Discipline:
- Science
-
- Creator:
- Kim, Wonhui, Ramanagopal, Manikandasriram Srinivasan, Barto, Charles , Yu, Ming-Yuan, Rosaen, Karl , Goumas, Nick , Vasudevan, Ram, and Johnson-Roberson, Matthew
- Description:
- PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.
- Citation to related publication:
- https://doi.org/10.48550/arXiv.1809.03605, https://github.com/umautobots/pedx, and http://pedx.io/
- Discipline:
- Engineering
-
- Creator:
- Salaree, Amir, Spica, Zack, and Huang, Yihe
- Description:
- The items in this bundle are supporting videos to a study of subsea seismo-acoustics carried out regarding an earthquake in the Persian Gulf. The main data used in the study is a diver's recording of the acoustic waves from the earthquake. The epicenter and topography data used in this study are publicly available as cited in the README.txt file.
- Keyword:
- Seismo-acoustics, Persian Gulf, Divers’ Microphones, Seismic Hazard, Early Warning
- Discipline:
- Science
-
- Creator:
- Umana, Maria
- Description:
- Functional trait data from six species of trees widely distributed across an elevational gradient in El Yunque, Puerto Rico.
- Keyword:
- SLA, LA, leaf thickness, wood specific gravity, crown volume
- Citation to related publication:
- Umaña, M. N. In review. The interplay of drought and hurricanes on tree recovery: insights from dynamic and weak functional responses. Forthcoming
- Discipline:
- Science
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- Creator:
- Limon, Garrett C.
- Description:
- The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection
- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works -
- Creator:
- Limon, Garrett C.
- Description:
- The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
Realm 3: Tumulus Excavations
User Collection
- Creator:
- Galaty, Michael
- Description:
- The PASH Data Collection is comprised of Five data “realms”: 1) Survey and site data, 2) Settlement excavations, 3) Tumulus (burial mound) survey and excavations, 4) Artifact analysis, and 5) Geological data. All databases, field notebooks, unit and profile drawings, photographs, photo descriptions, radiocarbon dates, and geophysical survey data related to the tumuli excavations have been made available in PASH Deep Blue Data Realm 3., Total size of all files: approximately 2 gigabytes Chapter(s) linked to: Eight Abbreviations: “T” = tumulus, as in T099; “S” = site, as in S006, The excavation methods employed by PASH at tumuli replicate those employed at settlements. Natural stratigraphy was followed where possible, and arbitrary stratigraphic levels were defined when necessary. Arbitrary stratigraphic levels at tumuli often exceeded the 10 cm interval used at settlements, due to the large number of large rocks that needed to be removed. Due to the numerous rocks, not all mound fill was screened; rather, we screened every third bucket through quarter-inch mesh. By contrast, all soil from features was screened. Soil was sampled for flotation and water screening from every level and feature, but unlike samples from settlements, it has not been processed and analyzed. Each tumulus, being roughly circular, was divided into quadrants along the cardinal directions, and 1-m baulks between quadrants were defined. Quadrants were excavated separately by level. Sometimes quadrants were excavated concurrently. Tumulus unit/level/feature designations are therefore preceded by tumulus (T000) and quadrant (Q000) numbers. Artifact provenience was recorded down to levels and features, with important in situ artifacts sometimes being mapped into level/feature drawings along x-y-z axes. Strata and artifacts were measured cm below surface using a dumpy level. All levels and features were drawn and photographed, individually and by quadrant. , In each mound we followed natural stratigraphy whenever and wherever possible. However, given the steep downward curves of many strata, following the slopes of mound surfaces, this was not always possible. Thus, it is likely that some levels combine artifacts from different mound strata. To control partially for this difficulty, quadrant levels were often subdivided into separate units on the interior or the exterior of mounds (designated “collections units” or CUs). Mound and grave architecture, when present, was left in place until fully defined and documented and then removed if necessary. Baulks were drawn in profile and photographed and removed en masse at the end of each excavation., and Prior to excavation, all mounds surveyed in Shtoj and Shkrel were mapped and fully documented. The state of preservation (present day and projected into the future) of each mound was recorded (from poor to excellent, and from fully safe to critically endangered). Given that so many mounds in both regions were already damaged or had been destroyed, or were actively threatened, we decided to excavate mounds that were (1) already completely removed (T-085), (2) damaged by agricultural activities (T-052), (3) going to be removed by a landowner (despite legal prohibitions) (T-088), and (4) previously looted or excavated (T-099). We did not want to excavate seemingly intact, undamaged mounds. Our tumulus excavations can therefore be aptly described as “rescue” excavations.
- Keyword:
- archaeology
- Discipline:
- Science and Humanities
5Works