Including dependent variables: likes, comments, collects, shares; Independent variables: perception of advertising disclosure, proportion of negative reviews, proportion of women, proportion of Gen Z audience, proportion of middle age audience, proportion of middle-aged and elderly audience; And control variables: release days, video duration, price

The mapping process as applied in this dataset provides a vector based inventort of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition. Mass movement is mapped where it exists, as is a selected range of weed species in pasture areas. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation’s full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying go different data sets. The full SCALD programme permits the coding of slope, terrain, land use, vegetation community, vegetation regeneration, tree and shrub canopy density, understorey status, projective foliage cover (McDonald et al. 1990), Western Region vegetation, soil erosion, mass movement, soil conservation earthworks, extent of rock outcrops, geology and Great soil groups., geology, great soil group, soil landscapes, physical limitations, land capability, soil depth, user defined attributes and Northwest vegetation associations. Soil landscapes information from the DLWC mapping program of the same name can be incorporated into the SCALD code set. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes.

Food purchases differ substantially across countries. We use detailed household level data from the US, France and the UK to (i) document these differences; (ii) estimate a demand system for food and nutrients, and (iii) simulate counterfactual choices if households faced prices and nutritional characteristics from other countries. We find that differences in prices and characteristics are important and can explain some difference (e.g., US-France difference in caloric intake), but generally cannot explain many of the compositional patterns by themselves. Instead, it seems an interaction between the economic environment and differences in preferences is needed to explain cross country differences.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.The Columbia County Planning Department has included a limited number of attributes with this dataset in order to make a more manageable layer.

The 2015 LU/LC data set is the sixth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012 and 2015. This present 2015 update was created by comparing the 2012 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2015 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2015 land use directly to the base data layer. All 2012 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2012-2015 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2015. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2015 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

from: https://archive.ics.uci.edu/ml/datasets/car+evaluation

1. Title: Car Evaluation Database

2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 1997

3. Past Usage:

   The hierarchical decision model, from which this dataset is derived, was first presented in

   M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988.

   Within machine-learning, this dataset was used for the evaluation of HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in

   B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear)

4. Relevant Information Paragraph:

   Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure:

   CAR car acceptability . PRICE overall price . . buying buying price . . maint price of the maintenance . TECH technical characteristics . . COMFORT comfort . . . doors number of doors . . . persons capacity in terms of persons to carry . . . lug_boot the size of luggage boot . . safety estimated safety of the car

   Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).

   The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.

   Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.

5. Number of Instances: 1728 (instances completely cover the attribute space)

6. Number of Attributes: 6

7. Attribute Values:

   buying v-high, high, med, low maint v-high, high, med, low doors 2, 3, 4, 5-more persons 2, 4, more lug_boot small, med, big safety low, med, high

8. Missing Attribute Values: none

9. Class Distribution (number of instances per class)

   class N N[%]

   unacc 1210 (70.023 %) acc 384 (22.222 %) good 69 ( 3.993 %) v-good 65 ( 3.762 %)

Please note that this file is large, ~450 MB, and may take a substantial amount of time to download especially on slower internet connections.Shapefile (NJ State Plane NAD 1983) download: Click "Open" or Click hereFile Geodatabase (NJ State Plane NAD 1983) download: Click hereThis data represents a "generalized" version of the 2007 LULC. To improve the performance of the web applications displaying the 2002 land use data, it was necessary to create a new simplified layer that included only the minimum number of polygons and attributes needed to represent the 2002 land use conditions. The 2007 LU/LC data set is the fourth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002 and 2007. This present 2007 update was created by comparing the 2002 LU/LC layer from NJ DEP's Geographical Information Systems (GIS) database to 2007 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2007 land use directly to the base data layer. All 2002 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2002-2007 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2007. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2007Land Use/Land Cover (LU/LC) data sets, which were mapped by Watershed Management Area (WMA). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

From the site: "This data set includes all soil map units that are defined as Hydric in the SSURGO data base.The SSURGO data set is a digital soil survey and is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was collected by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. The SSURGO data set consists of georeferenced digital map data and computerized attribute data. The map data are in a full county format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. Sometimes a special soil features layer (point and line features) is included. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Interpretations Record relational data base, which gives the proportionate extent of the component soils and their properties. | The data set has been provided to Chester County Departments and PASDA as an ArcView shapefile by the County of Chester, Department of Computer and Information Services. The theme has been reprojected to PA Stateplane (South) NAD83 from its original datum in accordance with the base map standards of the County of Chester. The County of Chester serves as the secondary organization in providing this shapefile, as compared to its originator and primary organization, the Natural Resources Conservation Service."

This data set was generated through the 2020 LU/LC update mapping effort. The 2020 update is the seventh in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012, 2015 and now, 2020. This present 2020 update was created by comparing the 2015 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2020 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2020 land use directly to the base data layer. All 2015 LU/LC polygons and 2015 LU/LC coding remains in this data set, so change analysis for the period 2015-2020 can be undertaken from this one layer. The mapping was done by USGS HUC8 basins, 13 of which cover portions of New Jersey. This statewide layer is composed of the final data sets generated for each HUC8 basin. Initial QA/QC was done on each HUC8 data set as it was produced with final QA/QC and basin-to-basin edgematching done on this statewide layer. The classification system used was a modified Anderson et al., classification system. Minimum mapping unit (MMU) is 1 acre for changes to non-water and non-wetland polygons. Changes to these two categories were mapped using .25 acres as the MMU. (See entry under the Advisory section concerning additional review being done on NHD waterbody attribute coding and impervious surface estimation.) ADVISORY This data set, edition 20231120, is a statewide layer that includes updated land use/land cover data for all HUC8 basins in New Jersey. The polygon delineations and associated land use code assignments are considered the final versions for this mapping effort. Note, however, that there is continuing review being done on this layer to update several additional attributes not presently evaluated in this edition. These attributes include several from the National Hydrography Database (NHD) that are specific to the waterbodies mapped in this layer, and several attributes containing impervious surface estimates for each polygon. Evaluating the NHD codes facilitates extracting the water features mapped in this layer and using them to update the New Jersey portion of the NHD. Those NHD specific attributes are still being evaluated and may be added to a future edition of this base data set. Similarly, additional review is being done to assess the feasibility of incorporating data on impervious surface (IS) amounts generated from two independent projects, one of which was just completed by NOAA, into this base land use layer. While the NHD and IS attributes will enhance the use of this base layer in several types of analyses, this present layer can be used for doing all primary land use analyses without having those attributes evaluated. Further, evaluating these extra attributes will result in few, if any, changes to the polygon delineations and standard land use coding that are the primary features of this layer. As such, the layer is being provided in its present edition for general use. As the additional attributes are evaluated, they may be added to a future edition of this data set. The basic land use features and codes, however, as mapped in this version of the data set will serve as the base 2020 LU/LC update. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.The data for Somerset County data was extracted & processed from the latest dataset by the Somerset County Office of GIS Services (SCOGIS).

Data Set Information: * The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls. * Acquisition date: January 8, 2015 * The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set. ##Attribute Information: Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) 0. url: URL of the article (non-predictive) 1. timedelta: Days between the article publication and the dataset acquisition (non-predictive) 2. n_tokens_title: Number of words in the title 3. n_tokens_content: Number of words in the content 4. n_unique_tokens: Rate of unique words in the conte

Sample data table of metrology attributes for management efficiency of commercial banks.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The partial data set includes a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection. An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code. The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011). Methods Data:

Raw files were downloaded from ftp1.cloudsat.cira.colostate.edu in directory 2B-GEOPROF.R04 Processed files are in netcdf format

Processing:

Data were processed and analyzed using IDL. See CloudSat_code_README.txt for details The initial processing was done while I was a graduate student at the Univerisity of Miami working on my masters from 2006-2009 Code is available at https://github.com/erileydellaripa/CYGNSS_code

Data file description:

Once the tar.gz file is unpacked, the EO attributes are provided in the EO_masterlistYYYY.nc files, where YYYY corresponds to the different years. I transferred the EO attributes from IDL .save files to netcdf files for sharing. A description of each EO attribute is provide in the README.md and if you do an ncdump -h in a terminal window.

The attributes are organized in 1D arrays, where the element of each array corresponds to a unique EO and the total size of the array corresponds to the total number of EOs identified.

Data are processed from the start of CloudSat 15 June 2006 till 17 January 2013 for the EO attributes.

In total, there are 15,181,193 EOs.

There was a battery failure 17 April 2011. CloudSat resumed collecting data 27 October 2011, but only during the day.

References:

Riley, E. M., B. E. Mapes, and S. N. Tulich, 2011: Clouds Associated with the Madden-Julian Oscillation: A New Perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051, https://doi.org/10.1175/JAS-D-11-030.1.

Riley, E. M., and B. E. Mapes, 2009: Unexpected peak near -15°C in CloudSat echo top climatology. Geophys. Res. Lett., 36, L09819, https://doi.org/10.1029/2009GL037558.

Riley, E. M., 2009: A global survey of clouds by CloudSat. M.S. thesis, Division of Meteorology and Physical Oceanography, University of Miami, 134 pp, https://scholarship.miami.edu/esploro/outputs/991031447848002976.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

The multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, …Show full descriptionThe multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey, soil erosion condition, and rockiness. Mass movement and soil conservation measures are mapped where they exist, as is a selected range of weed species. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying of different data sets. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes The information can, however, be useful as a first level of information in property planning exercises.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Group members gathered.