The Timely and Trustworthy Curating and Coordinating Data Framework (T2C2) project aims to dramatically reduce the materials-to-device process, which can currently span 20 years. Through the DIBBs program, funded by the National Science Foundation CISE Directorate and their Office of Advanced Cyber-Infrastructure (OAC), T2C2 will develop a new framework and system that focuses on the potential of capturing, curating, correlating and coordinating materials-to-devices digital data in a real-time and trusted manner before fully archiving and publishing the data for wide access and sharing. The software developed in this project is useful throughout the materials science and device fabrication fields, by automatically collecting, archiving, and providing collected information on all phases of materials and device fabrication development.
The project started in 2014 and ended in 2019. It develops the Timely and Trusted Curation and Coordination (T2C2) Data Framework, consisting of two data blocks:
1)The 4CeeD Curator, providing real-time acquisition and curation of digital data from selected materials-making / characterization and device-fabrication instruments in the collaborative research units at the university, the Material Research Lab (MRL) and the Micro-and-Nanotechnology Lab (MNTL) , and
2) The 4CeeD Coordinator, where collected data are filtered, correlations among data and dependency relations are identified, and the results are connected to other data processing capabilities. The goal of the T2C2 framework is to enable the reduction of the development time and cost of materials-making /characterization to device-making processes.
Through open-source software licenses and training programs, the project impacts material science, device fabrication and other fields within the university, and other interdisciplinary research institutions and their materials design and manufacturing processes. Through courses, tutorials, workshops, and outreach, the project develops interdisciplinary scientists, teaches the next generation of students, and informs broader audiences about the potential of timely and trusted data collection, curation, Spatio-temporal analytics, and correlations between material-making/characterization and device-fabrication processes.
4CeeD Mobile Beta, Now Available on Android
Jupyter Notebook Integration, Mobile friendly design, updated visualization/dashboards, LDAP login integration, metadata templates.
This research is funded by the National Science Foundation, NSF ACI 1443013, project title “CIF21 DIBBs: T2-C2: Timely and Trusted Curator and Coordinator Data Building Blocks.” Any results and opinions are our own and do not represent views of National Science Foundation.
The 4CeeD tool can only function when the instrument’s operating system (OS) is Windows 10 or higher, allowing instruments to have the computational capability and network speed to be part of a distributed cloud platform, and to have all the necessary security patches to be connected to the campus network. However, more than half of the major scientific instruments on our campus and their software tools run Windows 7, Windows XP, Windows NT, Windows 2000, and Windows 3.11, and thus are set offline because they cannot operate at the network speed of a powerful cloud and are not patched with the latest security patches.
This research was funded by the National Science Foundation (award number 1659293). The opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the National Science Foundation.
The cloud service 4Ceed and the BRACELET network accelerates the process of making scientific discoveries by providing researchers with the convenience to upload, examine, and process their experimental data (e.g., microscope images) and metadata (e.g., microscope settings). For researchers, equally important information towards correct scientific experimentation, besides instrument raw data and metadata, is sensory data around the instruments when experiments are conducted. For example, the ability to capture and control laboratory environmental sensory information such as temperature, humidity, vibration is crucial for nano-fabrication. In some laboratories, we have a few stand-alone sensors to collect humidity data. However, it is very time-consuming to manually collect and correlate those parameters with our fabrication process.
This research was funded by the National Science Foundation (award number 1827126). The opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the National Science Foundation.
Provenance is an appropriate solution to improve system security in research environments and cyberinfrastructures. Data provenance includes capturing the data/metadata origins, recording what happens to them, and monitoring where they move over time. Data provenance provides audit trails and helps to track the sources and reasons of any problems. Moreover, it increases the data trustworthiness by recording different transactions in the system and archiving them as log files. Data provenance also makes data replication easier and stored logs in the database can be simply used as a recipe to recreate the specific data. Provenance data let the system learn how the data was derived and this can be used for further investigations.
PROVLET project aims to introduce a new service that provides data provenance and group access control to the 4CeeD and its underlying data management system, Clowder. The solution not only defines provenance-based security but also considers securing the provenance data itself. It ensures that all the data and processes are securely accessed, tracked and archived in the system.
This research is funded by the National Science Foundation, NSF ACI 1835834, project title “ Collaborative Research: CSSI: Framework: Data: Clowder Open Source Customizable Research Data Management, Plus-Plus.” Any results and opinions are our own and do not represent views of National Science Foundation.
MAINTLET is a research project about an advanced sensory network cyber-infrastructure in scientific laboratories that enables cost-effective, scalable, and sustainable reactive, preventive and predictive maintenance solutions for scientific instruments.
This research is funded by the National Science Foundation, NSF OAC 21-26246, ” CC* Integration-Large” MAINTLET: Advanced Sensory Network Cyber-Infrastructure for Smart Maintenance in Campus Scientific Laboratories”. Any results and opinions are our own and do not represent views of National Science Foundation.