{"help":"Return the metadata of a dataset (package) and its resources. :param id: the id or name of the dataset :type id: string","success":true,"result":[{"id":"bf03ad36-2c14-4b5e-b5a8-d1a33a803278","name":"activity-recognition-model-sensor-gnss-time-and-motion-study-tm-and-individual-tree-data","title":"Activity Recognition Model: Sensor (AS), GNSS, Time and Motion Study (TM), and Individual Tree Data","author":"Ryer M. Becker \u0026amp; Robert F. Keefe","author_email":"rbecker@uidaho.edu ","maintainer":"RCDS Data Repository","maintainer_email":"rcds-web@uidaho.edu","license_title":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","notes":"The provided data are the raw sensor data gathered from the Google Pixel smartphones used during the field sampling for the development of the activity recognition models. Additionally, corresponding GNSS locations and observed cycle elements from\ttime and motion study are included and are all matched by time stamps. Data is provided based on the sampling rates used for the study (10, 20, 50Hz) These data are labeled (AS_TM_GNSS_\u0022date collected\u0022_\u0022sampling rate in Hz\u0022). Individual tree records derived from the lidar acquisition are also included for the study area. These data are labeled (Unit_#_Trees). The R code provided gives foundational R-code for random forest model training and final models for all windows (1, 5, 7.5, and 10 seconds) for each of the sampling rates (10, 20, and 50Hz) for natively sampled and downsampled (ds) data.","url":"https:\/\/data.nkn.uidaho.edu\/dataset\/activity-recognition-model-sensor-gnss-time-and-motion-study-tm-and-individual-tree-data","state":"Active","log_message":"Update to resource Becker_Keefe_2022_Data_Code","private":true,"revision_timestamp":"Tue, 01\/04\/2022 - 09:57","metadata_created":"Mon, 01\/03\/2022 - 10:59","metadata_modified":"Tue, 01\/04\/2022 - 09:57","creator_user_id":"6cc16f2c-77c2-4d12-ac06-56bbb86b535b","type":"Dataset","resources":[{"id":"eaf20d34-95c9-4cec-b680-24a0dcf4db0b","revision_id":"","url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/Becker_Keefe_2022_0.zip","description":"\u003Cp\u003Ezip directory that contains all the data and code for associated research manuscript submitted to PLOS ONE for review 01\/03\/2022\u003Cbr \/\u003E\n\tManuscript Title: A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations\u003C\/p\u003E\n\u003Cp\u003EData use:\u003Cbr \/\u003E\n\tLicense: CC-BY 4.0 \u003Ca href=\u0022https:\/\/creativecommons.org\/licenses\/by\/4.0\/\u0022\u003Ehttps:\/\/creativecommons.org\/licenses\/by\/4.0\/\u003C\/a\u003E\u003Cbr \/\u003E\n\tCitation for dataset reuse:\u003Cbr \/\u003E\n\t\tBecker, R., \u0026amp; Keefe, R. (2022). Activity Recognition Model: Sensor (AS), GNSS, Time and Motion Study (TM), and Individual Tree Data [Data set]. University of Idaho. \u003Ca href=\u0022https:\/\/doi.org\/10.7923\/1VQA-M812\u0022\u003Ehttps:\/\/doi.org\/10.7923\/1VQA-M812\u003C\/a\u003E\u003C\/p\u003E\n\u003Cp\u003EData Files:\u003Cbr \/\u003E\n\tBecker_Keefe_DOI_2022.txt:\u003Cbr \/\u003E\n\t\tDiscovery level metadata utilized to establish DOI for dataset\u003C\/p\u003E\n\u003Cp\u003E\tAS_TM_GNSS_Combined folder:\u003Cbr \/\u003E\n\t\tThe provided data are the raw sensor data gathered from the Google Pixel smartphones used during the field sampling for the\tdevelopment of the activity recognition models. Additionally, corresponding GNSS locations and observed cycle elements from time and motion study are included and are all matched by time stamps. Data is provided based on the sampling rates used for the study (10, 20, 50Hz) These data are labeled (AS_TM_GNSS_\u0026quot;date collected\u0026quot;_\u0026quot;sampling rate in Hz\u0026quot;).\u003Cbr \/\u003E\n\t\t\tData Files:\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_10_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_10_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_10_50.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_11_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_11_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_8_11_50.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_20_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_20_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_20_50.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_21_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_21_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_21_50.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_26_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_26_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_26_50.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_27_10.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_27_20.csv\u003Cbr \/\u003E\n\t\t\t\tAS_TM_GNSS_9_27_50.csv\u003C\/p\u003E\n\u003Cp\u003E\tSingle_Tree_Data folder:\u003Cbr \/\u003E\n\t\tIndividual tree records derived from the lidar acquisition for the study area. These data are labeled (Unit_#_Trees).\u003Cbr \/\u003E\n\t\t\tData Files:\u003Cbr \/\u003E\n\t\t\t\tUnit_1_Trees.csv\u003Cbr \/\u003E\n\t\t\t\tUnit_2_Trees.csv\u003Cbr \/\u003E\n\t\t\t\tUnit_3_Trees.csv\u003C\/p\u003E\n\u003Cp\u003ECode:\u003Cbr \/\u003E\n\tRandom_forests folder:\u003Cbr \/\u003E\n\t\tThe R code provided gives foundational R-code for random forest model training and final models for all windows (1, 5, 7.5, and 10 seconds) for each of the sampling rates (10, 20, and 50Hz) for natively sampled and downsampled (ds) data.\u003Cbr \/\u003E\n\t\t\tCode Files:\u003Cbr \/\u003E\n\t\t\t\tRandom_Forest_10hz_ALL_WINDOWS_ds.R\u003Cbr \/\u003E\n\t\t\t\tRandom_Forest_10hz_ALL_WINDOWS.R\u003Cbr \/\u003E\n\t\t\t\tRandom_Forest_20hz_ALL_WINDOWS_ds.R\u003Cbr \/\u003E\n\t\t\t\tRandom_Forest_20hz_ALL_WINDOWS.R\u003Cbr \/\u003E\n\t\t\t\tRandom_Forest_50hz_ALL_WINDOWS.R\u003Cbr \/\u003E\n\t\t\t\tTRAIN_Random_Forest_10hz_10_ds.R\u003Cbr \/\u003E\n\t\t\t\tTRAIN_Random_Forest_10hz_10.R\u003Cbr \/\u003E\n\t\t\t\tTRAIN_Random_Forest_20hz_10_ds.R\u003Cbr \/\u003E\n\t\t\t\tTRAIN_Random_Forest_20hz_10.R\u003Cbr \/\u003E\n\t\t\t\tTRAIN_Random_Forest_50hz_10.R\u003C\/p\u003E\n","format":"zip","state":"Active","revision_timestamp":"Tue, 01\/04\/2022 - 09:57","name":"Becker_Keefe_2022_Data_Code","mimetype":"application\/zip","size":"381.06 MB","created":"Tue, 01\/04\/2022 - 08:42","resource_group_id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","last_modified":"Date changed Tue, 01\/04\/2022 - 09:57"}],"tags":[{"id":"d4a22adb-fa9e-4181-a61e-a8a88c0e6517","vocabulary_id":"2","name":"activity recognition"},{"id":"ce84f0f8-f57d-4803-a3a3-c957a0ef5db9","vocabulary_id":"2","name":"inertial measurement units"},{"id":"ae0a046b-cc60-4e0e-bb9b-2e2369abeda6","vocabulary_id":"2","name":"IMU"},{"id":"9e1e6826-2de7-42c2-a0ca-89e93e719b2a","vocabulary_id":"2","name":"machine learning"},{"id":"a9f01e34-a21f-4cc3-81c4-93af51913e7f","vocabulary_id":"2","name":"forest operations"},{"id":"7170a284-f002-4fd4-99e6-817adda4d431","vocabulary_id":"2","name":"GNSS"},{"id":"7312fb89-d0e1-4b0a-83cd-fc98c287cf3d","vocabulary_id":"2","name":"lidar"}],"groups":[{"description":"\u003Cp\u003ESince 1889, the University of Idaho has provided motivated students with a transformative higher education experience that prepares them to solve real-world problems and achieve success in their lives and careers.\u003C\/p\u003E\n\u003Cp\u003ELearn more at \u003Ca href=\u0022https:\/\/www.uidaho.edu\u0022\u003Ehttps:\/\/www.uidaho.edu\u003C\/a\u003E\u003C\/p\u003E\n","id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/UI_Main_horizontal_4c.jpg","title":"University of Idaho","name":"group\/university-idaho"}]}]} Error | RCDS Data Repository

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