Wording changes for clarity

Author: nmdefries

docs/api/covidcast-signals/google-symptoms.md

@@ -21,7 +21,7 @@ nav_order: 1
 ## Overview
 
 This data source is based on the [COVID-19 Search Trends symptoms
-dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-search-trends?q=search&referrer=search&project=southern-guild-298314). Using
+dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-search-trends?hl=en-GB). Using
 this search data, we estimate the volume of searches mapped to symptom sets related
 to COVID-19. The resulting daily dataset for each region shows the average relative frequency of searches for each symptom set. The signals are measured in arbitrary units that are normalized for overall search users in the region and scaled by the maximum value of the normalized popularity within a geographic region across a specific time range. **Values are comparable across signals in the same location but NOT across geographic regions**. For example, within a state, we can compare `s01_smoothed_search` and `s02_smoothed_search`. However, we cannot compare `s01_smoothed_search` between states. Larger numbers represent increased relative popularity of symptom-related searches.
 

docs/api/covidcast-signals/safegraph.md

@@ -31,8 +31,7 @@ surface signals from two such datasets.
 
 **This dataset is no longer updated after April 19th, 2021.**
 
-Data source based on [Social Distancing
-Metrics](https://docs.safegraph.com/docs/social-distancing-metrics).  SafeGraph
+Data source based on the [Social Distancing Metrics](https://docs.safegraph.com/docs/social-distancing-metrics) data product.  SafeGraph
 provided this data for individual census block groups, using differential
 privacy to protect individual people's data privacy. 
 
@@ -131,7 +130,10 @@ COVIDcast API.
 
 ## Limitations
 
-SafeGraph's [Social Distancing Metrics](https://docs.safegraph.com/docs/social-distancing-metrics) and [Weekly Patterns](https://docs.safegraph.com/docs/weekly-patterns) are based on mobile devices that are members of SafeGraph panels, which is not necessarily the same thing as measuring the general public. These counts do not represent absolute counts, and only count visits by members of the panel in that region. This can result in several biases:
+SafeGraph's [Social Distancing Metrics](https://docs.safegraph.com/docs/social-distancing-metrics) 
+and [Weekly Patterns](https://docs.safegraph.com/docs/weekly-patterns) data products
+are based on mobile devices that are members of SafeGraph panels, which is not necessarily the same thing as measuring the general public. 
+These counts do not represent absolute counts, and only count visits by members of the panel in that region. This can result in several biases:
 
 * **Geographic bias.** If some regions have a greater density of SafeGraph panel members as a percentage of the population than other regions, comparisons of metrics between regions may be biased. Regions with more SafeGraph panel members will appear to have more visits counted, even if the rate of visits in the general population is the same.
 * **Demographic bias.** SafeGraph panels may not be representative of the local population as a whole. For example, [some research suggests](https://doi.org/10.1145/3442188.3445881) that "older and non-white voters are less likely to be captured by mobility data", so this data will not accurately reflect behavior in those populations. Since population demographics vary across the United States, this can also contribute to geographic biases.

docs/api/gft.md

@@ -18,10 +18,9 @@ General topics not specific to any particular endpoint are discussed in the
 ## Google Flu Trends Data
 
 Estimate of influenza activity based on volume of certain search queries. Google has discontinued Flu Trends, and this is now a static endpoint.
- - Data Source: [Google Flu Trends Estimates](https://www.google.com/publicdata/explore?ds=z3bsqef7ki44ac_)
+ - Data Source: [Google Flu Trends Estimates](https://www.google.com/publicdata/explore?ds=z3bsqef7ki44ac_) ([context](https://en.wikipedia.org/wiki/Google_Flu_Trends))
  - Temporal Resolution: Weekly from 2003w40 until 2015w32
  - Spatial Resolution: National, [HHS regions](http://www.hhs.gov/iea/regional/) ([1+10](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/regions.txt)); by state/territory ([50+1](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/states.txt)); and by city ([97](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/cities.txt))
- - Wikipedia Article: <https://en.wikipedia.org/wiki/Google_Flu_Trends>
  - Open access
 
 # The API

docs/api/ght.md

@@ -18,15 +18,12 @@ General topics not specific to any particular endpoint are discussed in the
 ## Google Health Trends Data
 
 Estimate of influenza activity based on volume of certain search queries.
+This data may be useful for real-time monitoring of diseases, as in:
 
-## Example Publications 
-
-Herman Anthony Carneiro, Eleftherios Mylonakis. [Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks](https://doi.org/10.1086/630200). Clinical Infectious Diseases, Volume 49, Issue 10, 15 November 2009, Pages 1557–1564. 
-
-Abel Brodeur, Andrew E. Clark, Sarah Fleche, Nattavudh Powdthavee.
+- Herman Anthony Carneiro, Eleftherios Mylonakis. [Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks](https://doi.org/10.1086/630200). Clinical Infectious Diseases, Volume 49, Issue 10, 15 November 2009, Pages 1557–1564. 
+- Abel Brodeur, Andrew E. Clark, Sarah Fleche, Nattavudh Powdthavee.
 [COVID-19, lockdowns and well-being: Evidence from Google Trends](https://doi.org/10.1016/j.jpubeco.2020.104346). Journal of Public Economics, Volume 193, 2021, 104346.
-
-Sudhakar V. Nuti, Brian Wayda, Isuru Ranasinghe, Sisi Wang, Rachel P. Dreyer, Serene I. Chen, Karthik Murugiah. [The Use of Google Trends in Health Care Research: A Systematic Review](https://doi.org/10.1371/journal.pone.0109583), October 2014.
+- Sudhakar V. Nuti, Brian Wayda, Isuru Ranasinghe, Sisi Wang, Rachel P. Dreyer, Serene I. Chen, Karthik Murugiah. [The Use of Google Trends in Health Care Research: A Systematic Review](https://doi.org/10.1371/journal.pone.0109583), October 2014.
 
 # The API
 

docs/api/nidss_dengue.md

@@ -36,7 +36,7 @@ See [this documentation](README.md) for details on specifying epiweeks, dates, a
 | Parameter | Description | Type |
 | --- | --- | --- |
 | `epiweeks` | epiweeks | `list` of epiweeks |
-| `locations` | **regions** (nationwide, central, eastern, Kaoping, northern, southern, Taipei) and **counties or cities** (Changhua County, Chiayi City, Chiayi County, Hsinchu City, Hsinchu County, Hualien County, Kaohsiung City, Keelung City, Kinmen County, Lienchiang County, Miaoli County, Nantou County, New Taipei City, Penghu County, Pingtung County, Taichung City, Tainan City, Taipei City, Taitung County, Taoyuan City, Yilan County, Yunlin County) | `list` of [region](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_regions.txt) and/or [location](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_locations.txt) labels |
+| `locations` | locations | `list` of [region](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_regions.txt) and/or [location](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_locations.txt) labels |
 
 ## Response
 

docs/api/nidss_flu.md

@@ -38,7 +38,7 @@ See [this documentation](README.md) for details on specifying epiweeks, dates, a
 | Parameter | Description | Type |
 | --- | --- | --- |
 | `epiweeks` | epiweeks | `list` of epiweeks |
-| `regions` | **regions** (nationwide, central, eastern, Kaoping, northern, southern, Taipei) | `list` of [region](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_regions.txt) labels |
+| `regions` | regions | `list` of [region](https://github.com/cmu-delphi/delphi-epidata/blob/main/labels/nidss_regions.txt) labels |
 
 ### Optional