The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

You can create a map for any area across the state by adding map layers of your choice to MassMapper, or view a single-topic map. MassGIS also has many maps and web services at ArcGIS Online. MassGIS does not provide any paper maps.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

Worcester Atlas is an interactive map viewer developed by the City of Worcester that gives the public access to city map layers and data, including property-specific assessor data.Users can search for property data by address, street, owner, or property ID, turn on/off map layers, get more information about certain layers in map popups, print maps, and more.More information: Visit the Introducing Worcester Atlas data story to get to know more about the City's map viewer.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

The PA Department of Conservation and Natural Resources (DCNR) and PA Game Commission (PGC) have teamed up to create an interactive map specifically for hunters. Collectively, State Forest Land and Gamelands comprise over 3.7 million acres of public forest open to hunting in Pennsylvania. Hunters can use this map to:View public forests open to hunting.Search hunting seasons and bag limits across different parts of the state.Display hunting hours (starting/ending times) across different parts of the state.Add personal GPS data to the map (waypoints and tracklogs).View different types of wildlife habitat across public forest lands, including mature oak forests, meadows, food plots, openings, winter thermal (coniferous) cover, and young aspen forest.See where recent timber harvests have occurred on public forest lands.Get deer management assistance program (DMAP) information for state forest lands.Add map layers associated with chronic wasting disease (CWD).Identify where bear check stations are located and get driving directions.Display the elk hunting zones and get information about them.Get the location of gated roads opened for hunters on public forest lands and when those gates will be opened.Analyze graphs and trends in antlerless/antlered deer harvests and antlerless license allocations from 2004 to the present.

Worcester Atlas is an interactive map viewer developed by the City of Worcester that gives the public access to city map layers and data, including property-specific assessor data.Users can search for property data by address, street, owner, or property ID, turn on/off map layers, get more information about certain layers in map popups, print maps, and more.More information: Visit the Introducing Worcester Atlas data story to get to know more about the City's map viewer.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

The Sanctuary Integrated Monitoring Network (SIMoN) is an integrated, long-term program that takes an ecosystem approach to identify and understand changes to the Monterey Bay National Marine Sanctuary. There are more than forty institutions and organizations in the greater Monterey Bay area that are currently examining various aspects of the Monterey Bay National Marine Sanctuary. Marine research conducted in the sanctuary includes long-term monitoring programs that are essential to furthering our understanding, and to determining the health, of the marine ecosystem. SIMoN enables researchers to monitor the sanctuary effectively by integrating the existing monitoring programs and identifying gaps in information. By avoiding duplication of these programs, resources can be more effectively directed towards surveying and characterizing habitats, assessing the impact of natural processes or human activities on specific resources, and long-term monitoring. Finally, SIMoN serves to make the monitoring data available to managers, decision makers, the research community, and the general public. Current projects, maps and graphs, and educational information are organized by subject on the website. In addition, three interactive maps are available which allow users to visualize, analyze and extract spatial data. The SIMoN Standard Viewer provides a wide variety of GIS data layers of various themes and focus. This viewer is useful for comparing spatial data from a wide variety of scientific disciplines. The SIMoN Water Quality Viewer provides GIS data layers relevant to water quality issues in and around the Monterey Bay National Marine Sanctuary. Finally the R/V McArthur II Research Cruise Viewer provides GIS data layers and links to video clips and images obtained from the April, 2004 McArthur II survey conducted in the Monterey Bay, Cordell Bank and Gulf of the Farallones national marine sanctuaries.

This dataset contains a topologically connected representation of the European high-voltage grid (220 kV to 750 kV) constructed using OpenStreetMap data. Input data was retrieved using the Overpass turbo API (https://overpass-turbo.eu). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of 500 m are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID way/83742802-1 can be viewed on OpenStreetMap using the query https://www.openstreetmap.org/way/83742802. A DC line/cable with the ID relation/15781671 can be accessed using the query https://www.openstreetmap.org/relation/15781671

A detailed explanation on the background, methodology, and validation can be found in the article published in Nature Scientific Data:

Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T. Modelling the high-voltage grid using open data for Europe and beyond. Sci Data 12, 277 (2025). https://doi.org/10.1038/s41597-025-04550-7

Countries included in the dataset:

Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)

The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

While the code and provided dataset in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the underlying input data.

Extract from OpenStreetMap Terms of Use

OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full legal code explains your rights and responsibilities.

Our documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0).

This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.

Changelog from version 0.5 to 0.6:

Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.

Interactive map.html now bundled with the dataset.

Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).

Changelog from version 0.4 to 0.5:

Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.

Clustering resolution improved from 5000 to 500 meters.

Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).

Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.

There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).

Wherever applicable, OSM IDs are preserved and used in the index of the network components.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

To get the data related in this map, see: https://www.bts.gov/geospatial/national-transportation-noise-map

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Real-time data and station plans of the Wiener Linien directly to your iPhone! Interactive map: Simply select a station in the map. There you will immediately get information and can take this station as a starting point for your route. Where is the lift at the station? We show you in the stations where you will find exits, elevators, staircases and toilets. Find out which stations are nearby! Plan the route! Interesting facts about your driving behavior! Never forget to get out again: We will send you a push message when the time comes!

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This feature layer provides access to OpenStreetMap (OSM) buildings data for Africa, which is updated every 1 minute with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM polygon (closed way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes building features defined as a query against the hosted feature layer (i.e. building is not blank).In OSM, a building is a man-made structure with a roof, standing more or less permanently in one place. These features are identified with a building tag. There are thousands of different tag values for building used in the OSM database. In this feature layer, unique symbols are used for several of the most popular building types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Streets level or 1:10k scale) to see the building features display. You can click on a feature to get the name of the building (if available). The name of the building will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this buildings layer displaying just one or two building types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. building is apartments), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. parks) that are ready to use, but not for every type of building.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

Perhaps you heard about Supreme Commander, an acclaimed Real Time Strategy game by Chris Taylor and now-defunct studio Gas Powered Games. Here's the original game trailer.

Over the years, an incredible community of enthusiasts came together and built a multiplayer game lobby and many other improvements, under the name FAF. Here's a video of a 2020 FAF match between two top rated players.

But now the cool bit. FAF has a scrapable API tracking every game ever played, and a parseable binary replay of every command issued by every player in every game. For example, here's the game metadata for that 2020 match, here's the map it was played on and here's its binary replay. I just had to scrape and publish that.

The dataset offers general metadata for ~10M FAF games (who played vs who, on which map, what's the scoreboard, etc) and deeper replay analysis of some 700K 1v1 games on the official FAF ladder (which type of units did the player use, how fast did they click, etc).

In addition to the data files you can download here, I publish all interim data products in BigQuery. The datasets I use are fafalytics.faf_ladder_1v1 and fafalytics.faf. Try SELECT * FROM `fafalytics.faf_ladder_1v1.playerstats_extended` in BigQuery to start exploring.

About the Author

I'm teolicy, occasional FAF player and hobbyist data geek. I played and loved Supreme Commander around 2010, but life got in the way and I kinda forgot about it. I discovered FAF in 2020 thanks to a wonderful 40th birthday gift. My wife was thinking "what can I get for my geek expat husband, living far away from his childhood friends during a global pandemic".

And she found FAF, and organised a surprise "LAN" party of my close friends (socially distanced across three continents...). In addition to being moved by the gift and the tenacious community keeping this game alive, I was also excited by the immense dataset of game metadata and replays that FAF collects.

So a while ago I started hacking on and off on data engineering tools to systematically scrape metadata from api.faforever.com, download and parse the replay blobs, and create coherent datasets fit analysis. The result is this dataset, the BigQuery public datasets, and a data engineering toolkit I made to create the datasets.

Acknowledgements

I received a lot of help from FAF developers (and permission from the DevOps team), but the work is independent from the FAF Association and isn't officially endorsed. This isn't legal advice, but as far as I can tell the data is governed by the FAF Terms of Service, which seem permissive enough.

This layer file consists of three related datasets:
- Statutory boundary polygons of State Forests
- Lands managed by the Division of Forestry within the statutory boundaries, known as Management Units
- Lands managed by the Division of Forestry outside of the statutory boundaries, known as Other Forestry Lands

State Forests - Statutory Boundaries:
This theme shows the boundaries of those areas of Minnesota that have been legislatively designated as State Forests ( http://www.dnr.state.mn.us/state_forests/index.html )

Minnesota's 58 state forests were established to produce timber and other forest crops, provide outdoor recreation, protect watersheds, and perpetuate rare and distinctive species of native flora and fauna. The mapped boundaries are based on legislative/statutory language and are described in broad terms based on legal descriptions. Private or other ownerships included inside a State Forest boundary are typically NOT identified in legislative language and subsequently are NOT mapped in this layer. It is important to note that these data do not represent public ownership. State Forest boundaries often include private land and should not be used to determine ownership. Ownership information can be found in State Surface Interests Administered by MNDNR or by Counties ( https://gisdata.mn.gov/dataset/plan-stateland-dnrcounty ) and the GAP Stewardship 2008 layer ( http://gisdata.mn.gov/dataset/plan-gap-stewardship-2008 ).

Data has been updated during 2009 by the MNDNR Forest Resource Assessment office.

State Forests - Management Units
This theme shows the land owned and managed by the Division of Forestry within the Statutory Boundaries. The shapes were derived mostly from county parcel data, where available, and from plat maps and other ownership resources. This data presents an approximate location of the land ownership and is intended for cartographic purposes only. It is not survey quality and should never be used to resolve land ownership disputes.

State Forests - Other Forest Lands
This theme shows State Forest lands outside of the State Forest Statutory Boundaries. It was derived from MNDNR's Land Records System PLS40 data layer. Sub-40 shapes are not represented. Partial PLS40 ownership is represented as a whole PLS40. This data is not survey quality and should never be used to resolve land ownership disputes.

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Sea Level Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Sea Level module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Coral Bleaching Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Coral Bleaching module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Coral Bleaching Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Coral Bleaching module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

The Cuyahoga County Greenprint is a collection of web-based tools intended to help planners, environmentalists, and community leaders make good nature- and land-use decisions by visualizing information about their natural and community assets in a map format.The interactive platform allows users to customize their views and extract data they need for grant applications, reports and community engagement events. We hope these Greenprint tools help you do your job and feed your passions for making Cuyahoga County a more sustainable and natural place.