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new file mode 100644
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+++ b/site/content/pages/datasets/ijb_c/index.md
@@ -0,0 +1,38 @@
+------------
+
+status: published
+title: IJB-C
+desc: IJB-C is a datset ...
+subdesc: The IJB-C dataset contains...
+slug: ijb_c
+cssclass: dataset
+image: assets/background.jpg
+year: 2015
+published: 2019-4-18
+updated: 2019-4-18
+authors: Adam Harvey
+
+------------
+
+## IARPA Janus Benchmark C (IJB-C)
+
+### sidebar
+### end sidebar
+
+Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor 
+
+Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor Loren ipsum dolor 
+
+{% include 'dashboard.html' %}
+
+{% include 'supplementary_header.html' %}
+
+
+{% include 'cite_our_work.html' %}
+
+### Footnotes
+
+[^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
+[^end_to_end]: Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. 
+[^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
+[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
diff --git a/site/content/pages/research/00_introduction/index.md b/site/content/pages/research/00_introduction/index.md
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--- a/site/content/pages/research/00_introduction/index.md
+++ b/site/content/pages/research/00_introduction/index.md
@@ -15,8 +15,11 @@ authors: Megapixels
 + Posted: Dec. 15
 + Author: Adam Harvey
 
+
 Facial recognition is a scam. 
 
+It's extractive and damaging industry that's built on the biometric backbone of the Internet.
+
 During the last 20 years commericial, academic, and governmental agencies have promoted the false dream of a future with face recognition. This essay debunks the popular myth that such a thing ever existed.
 
 There is no such thing as *face recognition*. For the last 20 years, government agencies, commercial organizations, and academic institutions have played the public as a fool, selling a roadmap of the future that simply does not exist. Facial recognition, as it is currently defined, promoted, and sold to the public, government, and commercial sector is a scam.
@@ -25,6 +28,15 @@ Committed to developing robust solutions with superhuman accuracy, the industry
 
 There is only biased feature vector clustering and probabilistic thresholding.
 
+## If you don't have data, you don't have a product.
+
+Yesterday's [decision](https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV) by Brad Smith, CEO of Microsoft, to not sell facial recognition to a US law enforcement agency is not an about face by Microsoft to become more humane, it's simply a perfect illustration of the value of training data. Without data, you don't have a product to sell. Microsoft realized that doesn't have enough training data to sell 
+
+
+## Use Your Own Biometrics First
+
+If researchers want faces, they should take selfies and create their own dataset. If researchers want images of families to build surveillance software, they should use and distibute their own family portraits. 
+
 ### Motivation
 
 Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to developing and validating face recognition technologies. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">videos on YouTube</a>.