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- Creator:
- Gary R. Kidd, Charles S. Watson, Brian Gygi. Indiana University, Bloomigton
- Description:
- The data show performance on 19 tests of auditory abilities, with 340 subjects. The data include: 1. Percent correct for each of the 19 TBAC tests used in the 2007 study 2. Arcsine transformed values of the PC scores (names ending in “AS”) 3. Percentile values for the Arcsine-transformed scores (names ending in “ASp”) 4. Latent variable scores for four independent auditory factors, plus a general auditory ability factor, as described in KWG 2007. Sex and race data are also included. For a subset of subjects, SAT scores, GPA, and an IQ estimate (based on SAT scores) is also provided.
- Keyword:
- auditory, speech, hearing, listening, individual differences
- Citation to related publication:
- Kidd, G. R., Watson, C. S., & Gygi, B. (2007). Individual differences in auditory abilities. The Journal Of The Acoustical Society Of America, 122(1), 418-435.
- Title:
- Individual Differences in Auditory Abilities: The Expanded TBAC dataset
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- Creator:
- Payne, Zachary C, Mushinski, Ryan M, Poehlman, John, Pusede, Sally E, and Raff, Jonathan D
- Description:
- Gas phase measurements of NO, NO2, N2O, CO2 and O3 were collected from four experimental chambers and 1m above the ground during a field campaign at the Indiana University Research and Teaching Preserve from 08/02/2017 to 08/18/2017. Chambers contained different levels of vegetation to understand the effect that vegetation has on soil fluxes of these gases. These measurements were processed in R computing environment to produce measurement of fluxes. Raw data and processed data both appear in this dataset.
- Keyword:
- Nitric Oxide, Nitrous Oxide, Nitrogen Dioxide, Soil Gas Flux, and Vegetation Effects
- Citation to related publication:
- JGR Atmosphere - Presubmission
- Title:
- Effects of Vegetation on Flux of NO, NO2, and N2O in a Mixed Deciduous Forest Clearing
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- Creator:
- Kaiyuan, Liu and Haixu, Tang
- Description:
- n/a
- Keyword:
- Spectrum Prediction, Tandem Mass Spectra, and Deep Neural Network
- Citation to related publication:
- http://www.predfull.com/datasets
- Title:
- Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network
-
- Creator:
- Niccum, Brittany A, Lee, Heewook, Ismail, Wazim Mohammed, Tang, Haixu, and Foster, Patricia L
- Description:
- Excel file format (xlsx)
- Keyword:
- evolution, mutation accumulation, and neutral mutation
- Citation to related publication:
- Title:
- List of base-pair substitutions in "The symmetrical pattern of base-pair substitutions rates across the chromosome in Escherichia coli has multiple causes"
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- Creator:
- Laughlin, Patrick M
- Description:
- Spectroscopy data were collected on a BioTek Synergy H1 plate reader, then exported to and analyzed in Excel. 3D charts used in the manuscript were generated with OriginPro. Microscopy images are in proprietary .nd2 format of Nikon Elements software. .nd2 files were opened and processed with ImageJ. Gels were imaged on a BioRad Chemidoc.
- Keyword:
- Nucleocapsid, N-Protein, N-protein, SARS-CoV-2, COVID-19, Coronavirus, and Virus Assembly
- Citation to related publication:
- Laughlin, P. M.; Young, K.; Gonzalez-Guiterrez, G.; Wang, J. C.-Y.,; Zlotnick, A. A narrow ratio of nucleic acid to SARS-CoV-2 N-protein enables phase separation. 2024.
- Title:
- Raw data for "A narrow ratio of nucleic acid to SARS-CoV-2 N-protein enables phase separation"
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabases with address points, street centerlines, parcels, county boundaries, and other assorted geographic units for years 2013, 2014, 2015, 2018. There is limited metadata for these data and they are not otherwise publicly available. Please contact the Indiana Geographic Information Office for more information.
- Keyword:
- Indiana GIS spatial data
- Citation to related publication:
- https://data-harvest-ingov.hub.arcgis.com/
- Title:
- 2013-2018 Indiana Statewide Geospatial Data Harvest
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2024. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase.
- Keyword:
- Indiana, GIS, spatial data
- Citation to related publication:
- https://dataharvest.gio.in.gov/
- Title:
- 2024 Indiana Statewide Geospatial Data Harvest
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2023. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase.
- Keyword:
- Indiana, GIS, and spatial data
- Citation to related publication:
- https://dataharvest.gio.in.gov/
- Title:
- 2023 Indiana Statewide Geospatial Data Harvest
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2022. Also includes zipped shapefiles for individual counties, state geocoder.
- Keyword:
- Indiana, GIS, and Spatial Data
- Citation to related publication:
- https://dataharvest.gio.in.gov/
- Title:
- 2022 Indiana Statewide Geospatial Data Harvest
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2021. Also includes zipped geodatabases for individual counties, state geocoder, and real property geodatabase. See inventory and metadata files for more information.
- Keyword:
- Indiana, GIS, and Spatial Data
- Citation to related publication:
- Title:
- 2021 Indiana Statewide Geospatial Data Harvest
-
- Creator:
- Indiana Geographic Information Office
- Description:
- Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2020. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase. See inventory file for full description of geodatabase layers, and metadata file for more information.
- Keyword:
- Indiana, GIS, and Spatial Data
- Citation to related publication:
- https://dataharvest.gio.in.gov/
- Title:
- 2020 Indiana Statewide Geospatial Data Harvest
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Archive of Indiana Statewide Geospatial Data
User Collection
- Creator:
- Quill, Theresa M
- Description:
- This collection contains yearly deposits of data from Indiana's Data Harvest - consisting of vector GIS layers for the state of Indiana as gathered and cleaned by the Indiana Geographic Information Office
- Keyword:
- GIS, Spatial Data, and Indiana Infrastructure
- Discipline:
- General Information Sources
6Works -
Indiana Sanborn Maps
User Collection
- Creator:
- Sanborn Map Company and Herman B Wells Library Map Collection
- Description:
- Sanborn Fire Insurance Maps were made for the interests of fire insurance companies, but because they are detailed, building-by-building depictions of most urban areas, they are useful for many kinds of research.
- Keyword:
- Sanborn Fire Insurance Map, Map, and Herman B Wells Library Map Collection
- Discipline:
- General Information Sources
284Sub-collections0Works -
- Creator:
- Doctor, Khoshrav, McKeever, Chaundy, Phadnis, Aditya, Wu, Di, Plawecki, Martin, Nurnberger Jr, John, and Jose, Jorge V
- Description:
- The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw. Description of the data and file structure. There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data: Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code: Information on how to analyze participants can be found in README.txt file within the folder. The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code: Information on how to perform the deep learning analysis can be found in README.txt file within the folder. The deep learning code is written in Python3 and relies on PyTorch.
- Keyword:
- Neurodivergent, Autism, Neurodevelopmental Disorder, Attention-Deficit/Hyperactivity Disorder, Biometric, Deep Learning, Motor deficits, kinesthetics , and Statistics
- Citation to related publication:
- K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
- Title:
- Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders
-
- Creator:
- Doctor, Khoshrav, McKeever, Chaundy, Phadnis, Aditya, Wu, Di, Plawecki, Martin, Nurnberger Jr, John, and Jose, Jorge V
- Description:
- The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw. Description of the data and file structure. There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data: Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code: Information on how to analyze participants can be found in README.txt file within the folder. The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code: Information on how to perform the deep learning analysis can be found in README.txt file within the folder. The deep learning code is written in Python3 and relies on PyTorch.
- Keyword:
- Neurodivergent, Autism, Neurodevelopmental Disorder, Attention-Deficit/Hyperactivity Disorder, Biometric, Deep Learning, Motor deficits, kinesthetics , and Statistics
- Citation to related publication:
- K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
- Title:
- Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders
-
- Creator:
- Gabler, Mikaela C; Martin, Bruce J; Johnson, Blair D; Schlader, Zachary J; and Chapman, Robert F
- Description:
- The purpose of this study was to examine how IMW affects the sensory and affective components of dyspnea, exercise performance, and NIRS-derived metaboreflex effects during a cycling time to exhaustion test. Additionally, to augment the ventilatory response for better elucidation of the cardiorespiratory effects of IMW, we added hypoxia as an intervention. Using both normoxic and hypoxic conditions, our hypotheses were: 1) both sensory and affective components of dyspnea would be attenuated following IMW in each condition, 2) the extent of skeletal muscle deoxygenation (i.e., a NIRS-derived surrogate for the metaboreflex) in the leg would be reduced after IMW in each condition, and 3) participants’ time to exhaustion would be prolonged following IMW in each condition.
- Keyword:
- altitude, breathlessness, dyspnea, metaboreflex, respiratory
- Citation to related publication:
- Title:
- Effects of inspiratory muscle warm-up on perceptual, physiological, and performance outcomes during high-intensity exercise in normoxia and hypoxia
-
- Creator:
- Gabler, Mikaela C; Martin, Bruce J; Johnson, Blair D; Schlader, Zachary J; and Chapman, Robert F
- Description:
- The purpose of this study was to examine how IMW affects the sensory and affective components of dyspnea, exercise performance, and NIRS-derived metaboreflex effects during a cycling time to exhaustion test. Additionally, to augment the ventilatory response for better elucidation of the cardiorespiratory effects of IMW, we added hypoxia as an intervention. Using both normoxic and hypoxic conditions, our hypotheses were: 1) both sensory and affective components of dyspnea would be attenuated following IMW in each condition, 2) the extent of skeletal muscle deoxygenation (i.e., a NIRS-derived surrogate for the metaboreflex) in the leg would be reduced after IMW in each condition, and 3) participants’ time to exhaustion would be prolonged following IMW in each condition.
- Keyword:
- altitude, breathlessness, dyspnea, metaboreflex, respiratory
- Citation to related publication:
- Title:
- Effects of inspiratory muscle warm-up on perceptual, physiological, and performance outcomes during high-intensity exercise in normoxia and hypoxia
-
- Creator:
- Chivukula, Sai Shruthi, Li, Ziqing, Pivonka, Anne, Chen, Jingning, and Gray, Colin M.
- Description:
- This dataset documents the outcomes of a research project where we identified, analyzed, and described a set of existing ethics-focused methods designed to support design research and practice for a range of audiences (such as technology and design researchers and practitioners, and educators). The final dataset includes 63 ethics-focused methods, describing the intended audience(s), format of guidance, interaction qualities, utilization of existing knowledge or concepts, implementation opportunities within design processes, and the "core" or "script" of the method.
- Keyword:
- design methods, ethics, values, and design practice
- Citation to related publication:
- https://arxiv.org/abs/2102.08909
- Title:
- Dataset of Ethics-Focused Design Methods
-
- Creator:
- Fulton, Ben. Hahn, Matthew W., Mendes, Fabio.
- Description:
- Here we present CAFE 5, a completely re-written software package with numerous performance and user-interface enhancements over previous versions. These include improved support for multithreading, the explicit modelling of rate variation among families using gamma-distributed rate categories, and command-line arguments that preclude the use of accessory scripts.
- Keyword:
- bioinformatics phylogenetics software
- Citation to related publication:
- 10.1093/bioinformatics/btaa1022
- Title:
- Tutorial Files for CAFE5
-
- Creator:
- Donnellan, Andrea, Lyzenga, Gregory, Wang, Jun, Pierce, Marlon, and Goulet, Christine
- Description:
- n/a
- Citation to related publication:
- www.geo-gateway.org
- Title:
- High-resolution Targeted 3D Imaging Postseismic Products of the Ridgecrest M6.4 and M7.1 Earthquake Sequence