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diff --git a/site/content/_drafts_/adience/index.md b/site/content/_drafts_/adience/index.md
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+------------
+
+status: draft
+title: Adience
+desc: <span class="dataset-name">Adience</span> is a ...
+subdesc: Adience contains ...
+slug: Adience
+cssclass: dataset
+image: assets/background.jpg
+year: 2007
+published: 2019-2-23
+updated: 2019-2-23
+authors: Adam Harvey
+
+------------
+
+## Adience Dataset
+
+### sidebar
+### end sidebar
+
+[ page under development ]
+
+- Deep Age Estimation: From Classification to Ranking
+    - https://verify.megapixels.cc/paper/adience/verify/4f1249369127cc2e2894f6b2f1052d399794919a
+    - funded by FordMotor Company University Reserach Program
+- Unconstrained Age Estimation with Deep Convolutional Neural Networks
+    - https://verify.megapixels.cc/paper/adience/verify/31f1e711fcf82c855f27396f181bf5e565a2f58d
+    - "we augment our data by sampling 1000 images for the age group of 0-20 from Adience [3]"
+    - the work was supported by IARPA and ODNI
+
+{% include 'dashboard.html' %}
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diff --git a/site/content/_drafts_/ibm_dif/index.md b/site/content/_drafts_/ibm_dif/index.md
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+------------
+
+status: draft
+title: IBM Diversity in Faces
+desc: <span class="dataset-name">IBM Diversity in Faces</span> is a person re-identification dataset of images captured at UC Santa Cruz in 2007
+subdesc: IBM Diversity in Faces contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance
+slug: IBM Diversity in Faces
+cssclass: dataset
+image: assets/background.jpg
+year: 2007
+published: 2019-2-23
+updated: 2019-2-23
+authors: Adam Harvey
+
+------------
+
+## IBM Diversity in Faces Dataset
+
+### sidebar
+### end sidebar
+
+[ page under development ]
+
+in "Understanding Unequal Gender Classification Accuracyfrom Face Images" researcher affilliated with IBM created a new version of PPB so they didn't have to agree to the terms of the original PPB.
+
+>We use an approximation of the PPB dataset for the ex-periments in this paper.  This dataset contains images ofparliament members from the six countries identified in[4] and were manually labeled by us into the categoriesdark-skinned  and  light-skinned.1Our  approximation  tothe PPB dataset, which we call PPB*, is very similar toPPB and satisfies the relevant characteristics for the study we perform.  Table 1 compares the decomposition of theoriginal PPB dataset and our PPB* approximation accord-ing to skin type and gender.
+
+{% include 'dashboard.html' %}
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diff --git a/site/content/_drafts_/lfw/index.md b/site/content/_drafts_/lfw/index.md
index 5d90e87f..ad43e2dd 100644
--- a/site/content/_drafts_/lfw/index.md
+++ b/site/content/_drafts_/lfw/index.md
@@ -18,6 +18,15 @@ authors: Adam Harvey
 ### sidebar
 ### end sidebar
 
+
+
+## Research notes
+
+- Used in https://verify.megapixels.cc/paper/feret/verify/8aff9c8a0e17be91f55328e5be5e94aea5227a35https://verify.megapixels.cc/paper/feret/verify/8aff9c8a0e17be91f55328e5be5e94aea5227a35 by Raythen BBN https://en.wikipedia.org/wiki/BBN_Technologies a military contractor
+-----
+
+## Old content
+
 [ PAGE UNDER DEVELOPMENT ]
 
 *Labeled Faces in The Wild* (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." 
diff --git a/site/content/_drafts_/megaface/index.md b/site/content/_drafts_/megaface/index.md
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+------------
+
+status: draft
+title: MegaFace
+desc: <span class="dataset-name">MegaFace</span> is a face recognition dataset created by scraping Flickr photo albums
+subdesc: MegaFace contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance
+slug: MegaFace
+cssclass: dataset
+image: assets/background.jpg
+year: 2007
+published: 2019-2-23
+updated: 2019-2-23
+authors: Adam Harvey
+
+------------
+
+## MegaFace Dataset
+
+### sidebar
+### end sidebar
+
+[ page under development ]
+
+*MegaFace (Viewpoint Invariant Pedestrian Recognition)* is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way."
+
+MegaFace is amongst the most widely used publicly available person re-identification datasets. In 2017 the MegaFace dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute).
+
+{% include 'dashboard.html' %}
+
+
+### Research notes
+
+Dataset was used in research paper funded by SenseTime
+
+- https://verify.megapixels.cc/paper/megaface/verify/380d5138cadccc9b5b91c707ba0a9220b0f39271
+- x
+
+From "On Low-Resolution Face Recognition in the Wild:Comparisons and New Techniques"
+
+- Says 130,154 Flickr accounts, but I got 48,382
+- https://verify.megapixels.cc/paper/megaface/verify/841855205818d3a6d6f85ec17a22515f4f062882
+
+> 2) MegaFace Challenge 2 LR subset:The MegaFace challenge 2 (MF2) training dataset [48] is the largest (in the numberof identities) publicly available facial recognition dataset, with4.7 million face images and over 672,000 identities. The MF2dataset is obtained by running the Dlib [29] face detector onimages from Flickr [68], yielding 40 million unlabeled faces across 130,154 distinct Flickr accounts. Automatic identity labeling is performed using a clustering algorithm. We per-formed a subset selection from the MegaFace Challenge 2training set with tight bounding boxes to generate a LR subsetof this dataset. Faces smaller than 50x50 pixels are gathered for each identity, and then we eliminated identities with fewer thanfive images available. This subset selection approach produced 6,700 identities and 85,344 face images in total. The extractionprocess does yield some non-face images, as does the originaldataset processing. No further data cleaning is conducted onthis subset.
+
+UHDB31: A Dataset for Better Understanding Face Recognitionacross Pose and Illumination Variatio
+
+- http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Le_UHDB31_A_Dataset_ICCV_2017_paper.pdf
+- MegaFace 1 used 690,572 and 1,027,060
+- MegaFace 2 used 672,057 and 4,753,320
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