cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/sac7-4g23
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.
DOI: 10.21227/v7hw-wv52
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.
DOI: 10.21227/ft3e-9n82
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named "DATASET-1 (224 X 224) Categorized.zip", was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.
DOI: 10.5281/zenodo.3660174
title: Database of Past and Present Alien Invasive Species-Related Policy and Legislation in South Africa

description: A full list of all laws relevant to biological invasions in South Africa since 1861
DOI: 10.5281/zenodo.3660175
title: Database of Past and Present Alien Invasive Species-Related Policy and Legislation in South Africa

description: A full list of all laws relevant to biological invasions in South Africa since 1861
DOI: 10.15468/bd7t0j
title: Pseudotelegeusis meloi sp. nov., the first Telegeusinae from Peru (Coleoptera: Omethidae, Telegeusinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Roza, André Silva, Constantin, Robert, Mermudes, José Ricardo Miras (2019): Pseudotelegeusis meloi sp. nov., the first Telegeusinae from Peru (Coleoptera: Omethidae, Telegeusinae). European Journal of Taxonomy 580: 1-13, DOI: https://doi.org/10.5852/ejt.2019.580
DOI: 10.15468/ykwcv5
title: A new western Atlantic snapping shrimp of the Alpheus macrocheles group (Caridea, Alpheidae) revealed by morphological, molecular and color data

description: This dataset contains the digitized treatments in Plazi based on the original journal article Soledade, Guidomar O., Terossi, Mariana, Scioli, Justin A., Mantelatto, Fernando Luis, Almeida, Alexandre O. (2019): A new western Atlantic snapping shrimp of the Alpheus macrocheles group (Caridea, Alpheidae) revealed by morphological, molecular and color data. European Journal of Taxonomy 581: 1-21, DOI: https://doi.org/10.5852/ejt.2019.581
DOI: 10.5281/zenodo.3967903
title: Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process-data

description: This file is the data used in the aritcle "Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process"
DOI: 10.5281/zenodo.3967902
title: Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process-data

description: This file is the data used in the aritcle "Analytical solution of apparent permeability of a laminated structure with cross flow in the transient flow process"
DOI: 10.5281/zenodo.3660200
title: New Categories of Activity Cliffs

description: 7465 activity cliffs (ACs) are provided including ACs with two substitution sites (ds-ACs), isomer ACs (iso-ACs), and ACs containing privileged substructures (PS-ACs). See readme.txt for further details.
DOI: 10.5281/zenodo.3660198
title: New Categories of Activity Cliffs

description: 7465 activity cliffs (ACs) are provided including ACs with two substitution sites (ds-ACs), isomer ACs (iso-ACs), and ACs containing privileged substructures (PS-ACs). See readme.txt for further details.
DOI: 10.4225/01/59b5e273ce0f1
title: Passive recovery of wood loads in rivers

description: Data set used to develop and drive a mass balance wood delivery model on the King River (NE Victoria, Australia). Data are total loads of wood measured from a complete census of two desnagged sections of the King River. Accumulation rates of wood due to different delivery mechanisms are used in the Monte Carlo simulation to predict the average length of…
DOI: 10.3886/e120462
title: National Neighborhood Data Archive (NaNDA): Socioeconomic Status and Demographic Characteristics of ZIP Code Tabulation Areas, United States, 2008-2017

description: This dataset contains measures of socioeconomic and demographic characteristics by US ZIP code tabulation area (ZCTA) for the years 2008-2017. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.
description: A ZIP code to ZCTA crosswalk must be used to combine this dataset with ZIP code geocoded data. Such a crosswalk is available on the UDS Mapper website at https://www.udsmapper.org/zcta-crosswalk.cfm. Sample code for merging the UDS Mapper crosswalk with NaNDA datasets is available on the NaNDA repository at https://www.openicpsr.org/openicpsr/project/120088/.
description: ZCTAs in the United States. Smallest Geographic Unit: ZIP code tabulation area
DOI: 10.3886/e120453
title: 2020 Region of Waterloo Survey

description: The Interactive Voice Response (IVR) survey was conducted by Professor Anthony Piscitelli with assistance from students at Conestoga College. The survey was funded by the Region of Waterloo. This survey was conducted by randomly sampling listed landline numbers and randomly dialing likely Waterloo Region cellphone numbers. Business numbers were removed from the sample of phone numbers before dialing. esults are considered accurate +/- 3.9%, 19 times out of 20. The margin of error on subsamples is higher. Sampling error also exists due to the mismatch created by the random dialing of phone numbers as opposed to random sampling actual Waterloo Region residents.<br>
description: Results of this survey have been weighted by age, gender, and city/township according to the 2016 census.
description: Response Rates: The results of this poll are based on an interactive voice response survey conducted Wednesday, July 15, 2020, from 10 am to 8 pm. A total of 1306 people completed the first question of the survey and 633 completed the entire survey. A total of 261,998 phone numbers were called as part of this campaign. A total of 120,426 of these numbers were active lines (i.e. the call was an active number that either generated no answer, an answering machine or the call was answered). A total of 30,670 people picked up the phone for a contact rate of 25%. A response rate of 0.6% was achieved. This rate was calculated by dividing those who completed the entire survey by active lines minus the 11% of lines that were assumed to be ineligible based on the responses to questions 1 and 2.<br>
description: telephone audio computer-assisted self interview (TACASI);
description: Smallest Geographic Unit: Waterloo Region, Ontario, Canada
description: The sample was created by randomly selecting Waterloo Region landlines as listed in a digital phone book. A sample of likely cellphone numbers was added by randomly selecting phone numbers that according to the Canadian Numbering Administrator were originally assigned to Waterloo Region. Listed phone numbers belonging to Waterloo Region businesses were removed from the list before calling.
DOI: 10.3886/e120453v1
title: 2020 Region of Waterloo Survey

description: The Interactive Voice Response (IVR) survey was conducted by Professor Anthony Piscitelli with assistance from students at Conestoga College. The survey was funded by the Region of Waterloo. This survey was conducted by randomly sampling listed landline numbers and randomly dialing likely Waterloo Region cellphone numbers. Business numbers were removed from the sample of phone numbers before dialing. esults are considered accurate +/- 3.9%, 19 times out of 20. The margin of error on subsamples is higher. Sampling error also exists due to the mismatch created by the random dialing of phone numbers as opposed to random sampling actual Waterloo Region residents.<br>
description: Results of this survey have been weighted by age, gender, and city/township according to the 2016 census.
description: Response Rates: The results of this poll are based on an interactive voice response survey conducted Wednesday, July 15, 2020, from 10 am to 8 pm. A total of 1306 people completed the first question of the survey and 633 completed the entire survey. A total of 261,998 phone numbers were called as part of this campaign. A total of 120,426 of these numbers were active lines (i.e. the call was an active number that either generated no answer, an answering machine or the call was answered). A total of 30,670 people picked up the phone for a contact rate of 25%. A response rate of 0.6% was achieved. This rate was calculated by dividing those who completed the entire survey by active lines minus the 11% of lines that were assumed to be ineligible based on the responses to questions 1 and 2.<br>
description: telephone audio computer-assisted self interview (TACASI);
description: Smallest Geographic Unit: Waterloo Region, Ontario, Canada
description: The sample was created by randomly selecting Waterloo Region landlines as listed in a digital phone book. A sample of likely cellphone numbers was added by randomly selecting phone numbers that according to the Canadian Numbering Administrator were originally assigned to Waterloo Region. Listed phone numbers belonging to Waterloo Region businesses were removed from the list before calling.
DOI: 10.3886/e120381
title: Demographic Analysis Workflow using Census API in Jupyter Notebook: 1990-2000 Population Size and Change

description: This archive reproduces a table titled "Table 3.1 Boone county population size, 1990 and 2000" from Wang and vom Hofe (2007, p.58). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses Census API to retrieve data, reproduce the table, and ensure reproducibility for anyone accessing this archive.<br><br>The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration and management. The Census API is used to obtain population counts from the 1990 and 2000 Decennial Census (Summary File 1, 100% data). All downloaded data are maintained in the notebook's temporary working directory while in use. The data are also stored separately with this archive.<br><br>The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. <br><br>The notebook features code to perform the following functions:install/import necessary Python packagesintroduce a Census API Querydownload Census data via CensusAPI manipulate Census tabular data calculate absolute change and percent changeformatting numbersexport the table to csvThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the Census API downloads. The notebook could be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).
description: Smallest Geographic Unit: US County
DOI: 10.3886/e120381v1
title: Demographic Analysis Workflow using Census API in Jupyter Notebook: 1990-2000 Population Size and Change

description: This archive reproduces a table titled "Table 3.1 Boone county population size, 1990 and 2000" from Wang and vom Hofe (2007, p.58). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses Census API to retrieve data, reproduce the table, and ensure reproducibility for anyone accessing this archive.<br><br>The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration and management. The Census API is used to obtain population counts from the 1990 and 2000 Decennial Census (Summary File 1, 100% data). All downloaded data are maintained in the notebook's temporary working directory while in use. The data are also stored separately with this archive.<br><br>The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. <br><br>The notebook features code to perform the following functions:install/import necessary Python packagesintroduce a Census API Querydownload Census data via CensusAPI manipulate Census tabular data calculate absolute change and percent changeformatting numbersexport the table to csvThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the Census API downloads. The notebook could be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).
description: Smallest Geographic Unit: US County
DOI: 10.7910/dvn/vzvgqr/5fb7zs
title: cost_path_new.shx

description: :unav
DOI: 10.7910/dvn/vzvgqr/ozzex2
title: cost_path_new.prj

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DOI: 10.7910/dvn/vzvgqr/rvzap6
title: fuzzy_vs1.ovr

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DOI: 10.7910/dvn/vzvgqr/4yaavp
title: fuzzy_vs_cl2.tif

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DOI: 10.7910/dvn/vzvgqr/yhz2ok
title: cost_path_new.sbn

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DOI: 10.7910/dvn/vzvgqr/i10bjf
title: fuzzy_memb.aux.xml

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DOI: 10.7910/dvn/vzvgqr/btsixn
title: fuzzy_VS.aux.xml

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DOI: 10.7910/dvn/vzvgqr/0cpky9
title: fuzzy_vs_cl2.tfw

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DOI: 10.7910/dvn/vzvgqr/cofyd6
title: eucl_dist.aux.xml

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DOI: 10.7910/dvn/vzvgqr/ercad6
title: fuzzy_vs1.aux.xml

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DOI: 10.7910/dvn/vzvgqr/k45xcn
title: cost_path_new.dbf

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DOI: 10.7910/dvn/vzvgqr/chtwqk
title: cost_path_new.cpg

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DOI: 10.7910/dvn/vzvgqr/opukxo
title: fuzzy_vs_cl2.tif.aux.xml

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DOI: 10.7910/dvn/vzvgqr/ew1vtm
title: fuzzy_vs_cl2.tif.ovr

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DOI: 10.7910/dvn/vzvgqr/luax5w
title: fuzzy_vs.ovr

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DOI: 10.7910/dvn/vzvgqr/zwcduj
title: cost_path_new.shp

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DOI: 10.7910/dvn/vzvgqr/gcdxjh
title: fort.dbf

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DOI: 10.7910/dvn/vzvgqr/fysrsu
title: stelae.sbn

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DOI: 10.7910/dvn/vzvgqr/lfj2wd
title: fort.prj

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DOI: 10.7910/dvn/vzvgqr/toef28
title: fort.shx

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DOI: 10.7910/dvn/vzvgqr/uskcjv
title: tombs.gwt

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DOI: 10.7910/dvn/vzvgqr/5oc9mv
title: tombanalysis.R

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DOI: 10.7910/dvn/vzvgqr/9d2bpy
title: stelae.shx

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DOI: 10.7910/dvn/vzvgqr/83wp3w
title: study_area.prj

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DOI: 10.7910/dvn/vzvgqr/imts5r
title: study_area.dbf

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DOI: 10.7910/dvn/vzvgqr/k78ekd
title: stelae.dbf

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DOI: 10.7910/dvn/vzvgqr/24p8ym
title: tombs.shx

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DOI: 10.7910/dvn/vzvgqr/ivsc42
title: tombs.shp

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DOI: 10.7910/dvn/vzvgqr/oqnltl
title: fort.cpg

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DOI: 10.7910/dvn/vzvgqr/cvhwmk
title: study_area.shx

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DOI: 10.7910/dvn/vzvgqr/7gkvem
title: study_area.shp

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DOI: 10.7910/dvn/vzvgqr/0sn4pi
title: stelae.shp

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DOI: 10.7910/dvn/vzvgqr/ebknp7
title: fort.sbn

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DOI: 10.7910/dvn/vzvgqr/otiifb
title: fort.shp

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DOI: 10.7910/dvn/vzvgqr/6p9pwa
title: fort.sbx

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DOI: 10.7910/dvn/vzvgqr/aegi8r
title: stelae.sbx

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DOI: 10.7910/dvn/vzvgqr/dlr560
title: tombs.dbf

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DOI: 10.7910/dvn/vzvgqr/v6xbip
title: Aggregated Data (ttest).tab

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DOI: 10.7910/dvn/vzvgqr/lqoo96
title: Random Sample Viewshed Data-1.tab

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DOI: 10.7910/dvn/vzvgqr/epxdjr
title: Kolmogorov-Smirnov Prepared Data-1.tab

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