Eight versions of COBRA-compliant SBML files are available for each Recon 2M.1 and Recon 2M.2 depending on the use of: MNXref versus BiGG IDs; Entrez gene IDs (GPR associations) versus Ensembl transcript IDs versus RefSeq transcript IDs versus UCSC transcript IDs (TPR associations for the last three database IDs).

892 personal GEMs (only T-GEMs) built with Recon 2.2

1,784 personal GEMs (both P-GEMs and T-GEMs) built with Recon 2M.1

892 personal GEMs (only T-GEMs) built with Recon 2M.2

Publication

Jae Yong Ryu1, Hyun Uk Kim1 & Sang Yup Lee*. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism., Proc. Natl. Acad. Sci. U.S.A., 2017, http://www.pnas.org/content/early/2017/10/23/1713050114

Our mission with this project is to provide an always up-to-date and freely accessible map of the cloud landscape for every major cloud service provider.

We've decided to kick things off with collecting SSL certificate data of AWS EC2 machines, considering the value of this data to security researchers. However, we plan to expand the project to include more data and providers in the near future. Your input and suggestions are incredibly valuable to us, so please don't hesitate to reach out on Twitter or Discord and let us know what areas you think we should prioritize next!

How to find origin IP of any domain or subdomain inside this database?

You can find origin IP for an example: instacart.com, Just search there instacart.com

You can use command as well if you are using linux. Open the dataset using curl or wget and then **cd ** folder now run command: find . -type f -iname "*.csv" -print0 | xargs -0 grep "word"

Like: find . -type f -iname "*.csv" -print0 | xargs -0 grep "instacart.com"

Done, You will see output.

How can SSL certificate data benefit you? The SSL data is organized into CSV files, with the following properties collected for every found certificate:

IP Address Common Name Organization Country Locality Province Subject Alternative DNS Name Subject Alternative IP address Self-signed (boolean)

IP Address Common Name Organization Country Locality Province Subject Alternative DNS Name Subject Alternative IP address Self-signed 1.2.3.4 example.com Example, Inc. US San Francisco California example.com 1.2.3.4 false 5.6.7.8 acme.net Acme, Inc. US Seattle Washington *.acme.net 5.6.7.8 false So what can you do with this data?

Enumerate subdomains of your target domains Search for your target's domain names (e.g. example.com) and find hits in the Common Name and Subject Alternative Name fields of the collected certificates. All IP ranges are scanned daily and the dataset gets updated accordingly so you are very likley to find ephemeral hosts before they are taken down.

Enumerate domains of your target companies Search for your target's company name (e.g. Example, Inc.), find hits in the Organization field, and explore the associated Common Name and Subject Alternative Name fields. The results will probably include subdomains of the domains you're familiar with and if you're in luck you might find new root domains expanding the scope.

Enumerate possible sub-subdomain enumeration target If the certificate is issued for a wildcard (e.g. *.foo.example.com), chances are there are other subdomains you can find by brute-forcing there. And you know how effective of this technique can be. Here are some wordlists to help you with that!

💡 Note: Remeber to monitor the dataset for daily updates to get notified whenever a new asset comes up!

Perform IP lookups Search for an IP address (e.g. 3.122.37.147) to find host names associated with it, and explore the Common Name, Subject Alternative Name, and Organization fields to gain find more information about that address.

Discover origin IP addresses to bypass proxy services When a website is hidden behind security proxy services like Cloudflare, Akamai, Incapsula, and others, it is possible to search for the host name (e.g., example.com) in the dataset. This search may uncover the origin IP address, allowing you to bypass the proxy. We've discussed a similar technique on our blog which you can find here!

Get a fresh dataset of live web servers Each IP address in the dataset corresponds to an HTTPS server running on port 443. You can use this data for large-scale research without needing to spend time collecting it yourself.

Whatever else you can think of If you use this data for a cool project or research, we would love to hear about it!

Additionally, below you will find a detailed explanation of our data collection process and how you can implement the same technique to gather information from your own IP ranges.

TB; DZ (Too big; didn't zoom):

We kick off the workflow with a simple bash script that retrieves AWS's IP ranges. Using a JQ query, we extract the IP ranges of EC2 machines by filtering for .prefixes[] | select(.service=="EC2") | .ip_prefix. Other services are excluded from this workflow since they don't support custom SSL certificates, making their data irrelevant for our dataset.

Then, we use mapcidr to divide the IP ranges obtained in step 1 into smaller ranges, each containing up to 100k hosts (Thanks, ProjectDiscovery team!). This step will be handy in the next step when we run the parallel scanning process.

At the time of writing, the EC2 IP ranges include over 57 million IP addresses, so scanning them all on a single machine would be impractical, which is where our file-splitter node comes into play.

This node iterates through the input from mapcidr and triggers individual jobs for each range. When executing this w...

GCOOS 52North Sensor Observation Service This station provides the following variables: Mass concentration of chlorophyll in sea water, Mass concentration of oxygen in sea water, Sea water practical salinity, Sea water temperature, Sea water turbidity

Non-traditional data signals from social media and employment platforms for RCON stock analysis

This dataset contains the predicted prices of the asset RECON over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

GCOOS 52North Sensor Observation Service This station provides the following variables: Mass concentration of chlorophyll in sea water, Mass concentration of oxygen in sea water, Sea water practical salinity, Sea water temperature, Sea water turbidity

This dataset contains the predicted prices of the asset Recon Labs over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

53 Global import shipment records of Ghost Recon with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains 10 second Air Force Recon flight level data collected during hurricanes Dennis, Emily, Gert, Katrina, Ophelia, Rita, and Wilma. Data is available for the following days: 2005 July 6-10 for Dennis, 2005 July 13-19 for Emily, 2005 July 24 for Gert, 2005 August 23-29 for Katrina, 2005 September 8 and 10-17 for Ophelia, 2005 September 18-24 for Rita, and 2005 October 16-21 and 23-25 for Wilma.

This dataset contains the predicted prices of the asset Recon Raccoon over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset provides information about the number of properties, residents, and average property values for Recon Lane cross streets in Saucier, MS.

Recon Robotics Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Dataset

This dataset was created by Nishant Sachdeva

Contents

Data accompanying the category learning drone recon study. Scripts and functions to assist in analysis are located in the github repo:

https://github.com/pettinelab/public_category_learning_metacog_management_analysis.git

Explore Recon Wood import export trade data. Find top buyers, suppliers, HS codes, ports, & market trends to make smarter, data-driven trade decisions.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Dataset

This dataset was created by Nishant Sachdeva

Contents

GCOOS 52North Sensor Observation Service This station provides the following variables: Mass concentration of chlorophyll in sea water, Mass concentration of oxygen in sea water, Sea water practical salinity, Sea water temperature, Sea water turbidity

423 Global export shipment records of Recon wood with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

View The recon group inc import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.