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Your topic should be narrow and if you used Wikipedia and history. SKIM the sources for keywords that relate to your topic. Academic journals, newspapers, magazines, biographies, autobiographies, government records, primary sources. Be wary of documentaries, since editing can promote a specific idea. Know who the director is. The most common question we receive is, how is SchoolBlocks inspired by Pinterest? It creates a unique and engaging browsing experience, optimizes for mobile and removes a lot of navigational complexity. Please click on the red plus signs found on this page to learn more about specific features.

No need to download an app! To view the mobile experience , please shrink your browser window by undocking the browser in order to make it resizable then resize the browser to be the size of an iPad then an iPhone. By clicking on your name, you find your personal tools including a combined calendar, feed and more.

This admin button appears wherever you have rights to alter a set of blocks. SchoolBlocks offers a unique and powerful personalized search system. A member of the public, searching for "homework" might see an announcement from the school as the first result. Welcome to the SHS Library 9 months ago. Please click on link to offer suggestions for our library! MYP Personal Project 2 months ago. Tips and Suggestions for your final report. Criteria for Success Criterion B and D.

Tools to help students follow their passion. Click HERE for project ideas. Photography and the Colorado Seasons. Discovery, Use, Production, and Effects. Remember, at the end of the project you will need these three items. If you need help with anything, please stop in the library. The Process Journal-- Your process journal is the record of your involvement in the Personal Project process. It should record all your ideas, your planning, your discussions, clippings of readings and concepts, diary excerpts, meeting schedules and outcomes with your supervisor, photographs — whatever you do that is part of the process should be kept in this document.

You choose the format of your journal. Approved Websites for "Hot Topics" Assignment Open this document for click-able links to reliable websites for your researching purposes. Google Doc - Thousand Cranes. You work in a packaging department of a food company. Until now you have mostly designed soup cans but the company has asked you to design the packaging for their ice cream. Because of your experience you know that the way products are packaged can have a significant impact on profits. For some products, the appearance of the package is important.

For other products, the primary concern is the cost of the package. Your task in this project is to design a package of ice cream that is made from the least amount of material while holding a specific volume. Web Based Presentation Tools over 2 years ago Click here to link to a wiki which provides you a TON of useful tools for presentations, graphing, audio, etc. Dogpile searches multiple search engines for your query, a "metasearch" Excite a popular broad search engine for the web Go Network powered by Infoseek , filters adult content Hotbot another popular engine with special search features Kartoo a highly visual meta-search engine, that presents results on a map Looksmart can do category and keyword searches by location Lycos find graphics, music files, and other specialized content Metacrawler searches multiple search engines for your query, a "metasearch" Northern Light search results are organized by "custom search folders" Webbrain uses concept mapping to make subject categories of your search more visible Webcrawler a simple, concise meta-search engine Yahoo Most popular directory ALMANACS Information Please Almanac Find answers to your questions in this wide ranging reference work, which includes an almanac, encyclopedia, and dictionary-- plus lots more.

Archival Research Catalog - U. Bubl - selected Internet resources covering all academic areas. Chemxseer - "documents in the chemistry domain". Citeulike - search over 5 million articles. The Collection of Computer Science Bibliographies - computer-science related information. Food Science Central - " world's largest database of information on food science, food technology and nutrition.

History Engine - research historical topics. Infomine - annotated academic sites and subject databases. Infotopia - educator selected sites. Intute - annotated academic sites. IPL - information you can trust. LibGuides Community - over , pathfinders from thousands of libraries. Library of Congress - search for primary source documents, including photos, maps, manuscripts, historic newspapers and much more.

PsycLine - psychology and social science journals. RefSeek - currently in public beta search over 1 billion documents, web pages, encyclopedias, journals, newspapers. Scirus - scientific information. SurfWax - search your topic and find similar, broader or narrower ones. Sweet Search - selective searches for students.

Virtual Learning Resources Center - browse the 12 category directory or use a Google custom search. Creative Commons and Public Domain Images over 2 years ago. About Creative Commons Licenses. Government employees for their work. Burning Well - public domain image source.

The Commons on Flickr - world public photo collection. Compfight -searches Flickr for CC images. EduPic Graphical Resource - free image resource for educators and students. FedFlix - "best movies of the United States Government, from training films to history, from our national parks to the U. Fire Academy and the Postal Inspectors, all of these fine flix are available for reuse without any restrictions whatsoever. Geograph -photos of the British Isles. Google Advanced Search - when you search something in the advanced mode, you can choose from four choices: Icons - from Crystal Clear, this is a huge and really nice collection of icons.

Imagebase - with hundreds of pages of images; from photographer David Niblack. Karen's Whimsy - Easter bunnies, illuminated letters, Canterbury Tales, medieval art. Library of Congress Prints and Photographs. LiveBinders - many, many more links to other image sites under CC. MorgueFile - search images by subject, size or rating. National Digital Library - from the U.

Fish and Wildlife Service. Open Clip Art Library - about 11, total. Open Icon Library - over free 10, icons. The Noun Project - symbols, diagrams. Photos8 - free stock pictures; close to two-dozen categories to choose from. Pics4Learning Pixabay - stunning photos and clipart. Public Domain Photos - high resolution.

Public Domain Photos - from imcphoto. Teaching Students About the Creative Commons. Wikimedia Commons - browse their database of over 1. Wylio - free pictures for bloggers. Primary Sources over 2 years ago Milestone Documents - from America in the s. The American Folklife Center. American Memory from the Library of Congress - browse collections by time period, place, topic; search through map collections, manuscripts, motion pictures, sheet music, photos, prints, sound recordings and books or other printed texts.

American Rhetoric - online speech bank. Archives in the Attic - documents from the Great Depression. The Authentic History Center - artifacts and sounds from American popular culture. Bancroft Library at the University of California, Berkeley - 0ver 60 million manuscript items and , volumes; huge collection. Biblioteche Saint Genevive - blind-tool bookbindings from 12thth centuries. Child Labor in America - covers The Coming of the American Revolution - from the Massachusetts Historical Society; 14 lessons include primary source documents.

Documents for the Study of American History - from , this is a very extensive listing of pdf and audio files. Eurodocs - selected transcriptions, facsimiles and translations. American Originals from the National Archives. Eyewitness to History - audio clips from people entitled "Voices of the 20th Century". Headlines and Audio from Historical Text Archive - over 9, articles, books and links.

History of Ireland - primary documents. History Matters - includes 1,00 primary documents, images and audio interviews. There are substantial challenges at the technical level for imaging in TBI. The main challenge is standardization or how to take into account differences between various laboratories using different acquisition rates, resolutions, scanning parameters, among others[ 79 ]. Even identical scanners used at different locations can generate differences in the quality of primary data, which in combination with different atlases and analytical programs make large-scale comparative studies challenging.

BDA approaches will play a major role in establishing such a model by combining incoming high fidelity imaging data that includes white matter connectivity and functional activity in addition to basic anatomical information. The amount of data from TBI studies will increase exponentially as more and more institutions are using scanners at increasing frequency.

Analyzing imaging data to find correlations between structural and molecular changes biomarkers and neurobehavioral outcomes represents a serious challenge due to the size and varying structures of data. Connecting cellular biomechanics data to data derived from animal modeling and applying the combined knowledge to clinical TBI would substantially increase our understanding about the physical to biological coupling down to the molecular level, which would guide evidence-based therapies.

The physical forces encountered by individual cells will determine survival or death, and in the case of survival, it initiates a complex molecular response to recover and regenerate. In contrast to other systems, such as the vascular system, which is constantly exposed to mechanical loading, stretch during the cardiovascular cycle, neurons and glia are mechanically naive and protected. Cell culture models [ 87—89 ] in combination with various outcome measures have provided critical insights into cellular and subcellular responses to mechanical forces. Data from these experiments are used to build in silico models of TBI [ 90—92 ].

However, the human brain contains approximately billion neurons and ten-times more glial cells. Thus, high-fidelity modeling of TBI that includes detailed molecular responses to mechanical forces cannot be accomplished without considerable use of BDA approaches. From the biomechanical perspective, the cranium and the intracranial structures such as the dura and pia mater, and the trabeculae provide the first line of defense in mitigating the physical impact and have distinct tissue properties from the cerebrum and cerebellum proper, each responding to the physical impact in different ways [ 93—95 ].

Such fault lines exist at the cellular and subcellular levels due to different material properties, elasticity, compressibility of axons versus cell bodies; capillaries, endothelial cells versus astroglial foot processes; myelin sheaths versus axons. These fault lines are the anatomical substrates for the vascular and axonal injuries triggered by the physical insult [ 96—98 ]. Accordingly, in addition to the actual g -forces, the 3D distribution of the g -force is critical in determining the biological response to the mechanical forces further increasing the number of data elements.

One of the greatest challenges in TBI is to improve translatability of experimental data into clinical practice. There have been numerous animal models developed over the last several decades that have attempted to mimic clinically observed conditions. They are categorized as focal, diffuse, penetrating, blast, among otherstypes of injuries with each focusing on a specific type of physical impact. Animal modeling of TBI including issues with current models has been recently reviewed in an excellent book chapter [ ] and is beyond the scope of this review.

In addition, basic biology, physiology and pathobiology of rodents are significantly different from humans, so without employing BDA approaches, the correlation and relevance between experimental findings and clinical cases remain rather subjective guesswork.

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These issues are known but are easily addressable gaps in outcome measures between experimental and clinical TBI research. The physical forces are known for in vivo TBI studies but, without the BD approach, we currently lack the ability to combine in vitro and in vivo data aimed to identify the pathobiology of TBI at the cellular, subcellular and molecular levels. Full implementation of CDE is not yet in sight.

Moreover, it would take enormous amounts of time, effort and funding to load existing, unstructured data into traditional relational databases such as FITBIR. Powerful tools using BD and BDA approaches are available and they have been successfully employed in counter-terrorism efforts, fighting crime, bank fraud, among others. Tools developed by these and other companies, such as IBM provide predictions, relationships and correlations by analyzing giant, messy, unstructured incomplete datasets.

Tools such as those developed and used by, for example, Palantir Technologies can uncover terrorist networks and plans to prevent attacks using far less reliable, more fragmented, less structured information available to the intelligence community than is available to the TBI community. Using similar tools may uncover correlations and connections addressing some of the TBI field's most pressing issues. A data lake is a storage repository that contains a huge amount of raw data in its native, unstructured format in so-called flat nonhierarchical architecture.

Data elements are tagged with a unique identifier and extended metadata tags. Such a data lake can be queried then with a specific question and a smaller dataset can be analyzed to answer a specific question. From the BD perspective, in addition to their unstructured formats, there are other issues with the published TBI or virtually any other biomedical literature [ 7 , 12—13 ]. Publications represent only a fraction of total data collected and accumulated during experimental TBI works or clinical studies, and they are curated.

The current total TBI literature published in peer-reviewed journals is just over 33, papers. If we take into account the previously mentioned dark data, completed but unpublished works and additional information, the number is close to , in text heavy, unstructured data format. Current mining of this legacy data is performed manually using PubMed or similar search engines involving heavy use of the searcher's skills as well as judgments, and so this approach is prone to be subjective and biased.

Successful use of BDA approaches will require three critical changes in our current practices and thinking: It should be noted that employing BDA will originally reveal only correlations but not causations, but the accumulated correlations over time will allow establishment of causative relationships. A lot of evenly unstructured data with uneven quality is better than a small, curated dataset. Experimental preclinical and clinical TBI studies use different outcome measures, methodologies, different species with different anatomy, physiology, different physical forces resulting in an enormous gap between the two fields.

There are somewhat similar functional assays such as Injury Severity Score, Neurological Severity Score, among others, available for rodents in experimental TBI, but we do not know how these correlate with the gold standard Glasgow Coma Scale and other clinical tests [ ]. The resulting paradox is that we know the extent of the functional deficits, but not the parameters of the causative physical forces in clinical TBI; whereas, in experimental TBI, we know the parameters of the physical forces we can calibrate them , but not functional deficits caused by these forces.

The approach to collect both physical data using sensors as well as monitoring functional deficits and collecting biochemical data as outlined above will help to identify the correlation between physical impact and biological response. But the field badly needs BD approaches to establish the correlation between injury-induced changes, functional deficits, biochemical and structural changes detected in humans and in rodents using already available data.

Only after we understand the correlation between injury-induced changes in clinical versus experimental TBI can we translate promising preclinical pharmacotherapies into clinical trials with high fidelity, and vice versa can we design more clinically relevant experimental studies. As we collect more and more data, for example, from patient monitoring at NICUs [ 40 ] or physical, biological and clinical data from athletic fields following concussions, we will be able to understand correlations.

In biology or in diseases such as TBI, most events are probabilistic rather than certain. Imagine, for example, if all NICUs collected and stored all the functional, imaging and biochemical, and similiar data from all patients [ 40 ]. Or, all the physical, biochemical, imaging and functional data related to concussion were collected at the sidelines of athletic fields. Or, if in experimental TBI, animals were being monitored for outcome measures mirroring clinical practices and all of the data were collected and stored along with unique experimental data such as histopathology [ ].

Using BDA would enable us to use the collected BD to discover correlations between, for example, altered cerebral perfusion pressure, cerebral blood flow, cerebral metabolic rate of oxygen, cerebral metabolic rate of glucose consumption and long-term functional outcome; or to discover correlations among g -force, directionality, biochemical changes, functional impairments and long-term outcome; or to discover correlations between biochemical and functional outcome measures and cellular and molecular levels of pathological changes bringing experimental TBI studies closer to clinical needs.

However, we must accept that the collected data will be incomplete and messy. But, large volumes of incomplete, messy data are more valuable and enable higher probability of correlation than clean, curated small datasets that may or may not be representative. Maybe the greatest challenge for us as TBI researchers is to change our habits. Such changes should include recording and storing everything digitally so we can reduce the amount of dark data. We must make all the data available for re analysis. This is especially true for mild TBI, where the individual may or may not be taken to the ER, and may or may not be seen by a concussion specialist.

Accordingly, most of the data will remain incomplete, unformatted, fragmented and unstructured so we need to seriously think about employing Palantir, Ayasdi, etc.

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On the experimental side, there is a goldmine of published legacy data available, also unstructured, messy and incomplete and only BDA can help to find the correlations we so much need to improve protection and patient care. This new approach will transform the current practice of triaging, diagnostics, treatments and prognosis into highly integrated, evidence-based patient care.

The physical forces will be measured and recorded by using the next generation of sensors. Streaming real-time data from sensors will generate a whole new level of data both in quantity and in quality. Welcome to the future! The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript. This work is licensed under the Creative Commons Attribution 4. To view a copy of this license, visit http: National Center for Biotechnology Information , U. Journal List Concussion v. Published online Jul Author information Article notes Copyright and License information Disclaimer.

Received Jul 15; Accepted May This work is licensed under a Creative Commons Attribution 4. Abstract Traumatic brain injury TBI is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. Big Data BD is a term for extremely large datasets that are so large and complex that they cannot be analyzed using traditional data processing applications [ 3—15 ].

Traumatic brain injury TBI is a spectrum disease. Current use of BD in TBI Efforts to improve the clinical practice guidelines to assess the severity of concussion have resulted in the development of several algorithms to evaluate changes in physical, cognitive, behavioral, imaging and neuropsychological levels [ 33 ]. Some potential data sources for Big Data Analytics in experimental traumatic brain injury. Source Description Strengths Limitations Animal characteristics Species, age, sex, weight Homogeneous population, reproducibility Gender and age biased mostly young males used , translational value is an issue, unstructured data Animal history and injury model Experimental details, surgery, modeling closed, open, rotational, focal , etc.

Objective measures of changes in physiology, structured data, specific functional impairments and translational relevance Physiological monitoring is rarely used in experimental TBI, neurobehavioral data are investigator dependent and unstructured Imaging Various modalities CT, MRI, PET Clinically relevant, repeatable, noninvasive, provides morphological molecular PET information Rarely performed in experimental TBI, no standardized analysis programs, difficulties comparing data from different laboratories, very large data Biochemical markers Injury-induced changes in serum or CSF or bECF levels of metabolites, nucleic acids and proteins Can identify the molecular pathology of the injury process, can identify targets for therapeutics.

Open in a separate window. Some potential data sources for Big Data Analytics in clinical traumatic brain injury. Overview of potential Big Data Analytics approaches in experimental traumatic brain injury. Overview of potential Big Data Analytics approaches in clinical traumatic brain injury. Sensors Because TBI is caused by physical forces that can be measured and quantified, an important step toward using BD approaches and developing a TBI dosimetry is to understand the correlation between the physical forces and the biological response [ 45 ].

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Biochemical markers The presence of brain-derived proteins in the blood or cerebrospinal fluid CSF can provide objective measures for determining TBI's severity, identifying the pathomechanisms of the secondary injury process and predicting recovery [ 29 , 44 , 68—71 ]. Imaging With significant and rapid advances in the field, including the technology to acquire various imaging modalities coupled with high-speed image processing and analytics programs, in vivo imaging represents a perfect example of promises and challenges of using the BDA approach in TBI research, diagnosis and management.

Challenges Successful use of BDA approaches will require three critical changes in our current practices and thinking: Mild TBI concussion is the most common type of TBI and when sustained repetitively increases the risk of developing neurodegenerative disorders. Biological responses to TBI occur at multiple levels including structural, physiological, behavioral and molecular that manifest in complex and dynamically changing clinical symptoms.

It appears that the current approach of analyzing small, representative, curated data will not be able to provide solutions to the complexity of the disease. BD approaches can use unstructured, incomplete, irregular and ambiguous data as long as the dataset is large. Analyzing BD using Artificial Intelligence, Machine Learning and Cognitive Computing can identify new correlations not available by traditional approaches. BD has been successfully employed in fields of logistics, counter-terrorism efforts and healthcare.

BD TBI requires collecting and storing all data elements, physical, biological, structural and clinical that may not be curated and may remain incomplete.

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Employing Big Data Analytics BDA in TBI can reveal correlations between physical forces collected by sensors, biological responses such as imaging, functional impairments, molecular pathologies and their temporal patterns. Employ or customize existing BDA approaches such as Palantir or Ayasdi that have been proven successful in identifying correlations using similar complex data.

An important proof of concept of such a BDA approach would be to establish correlations between experimental and clinical data. Open access This work is licensed under the Creative Commons Attribution 4. Centers for Disease Control and Prevention. Big data for health. Chaussabel D, Pulendran B. A vision and a prescription for big data-enabled medicine. Coffron M, Opelka F. Big promise and big challenges for big heath care data. Big data from small data: From big data to smart data in Alzheimer's disease.

The brain health modeling initiative to foster actionable knowledge. Integrative methods for analyzing big data in precision medicine. Harnessing big data for health. Putting big data to good use in neuroscience. Integrating medical and research information: Wang W, Krishnan E. Big data and clinicians: Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data. Classification of traumatic brain injury: Faul M, Coronado V.

Epidemiology of traumatic brain injury. The impact of traumatic brain injuries: Changing patterns in the epidemiology of traumatic brain injury. Outcome after traumatic brain injury improved by an organized secondary insult program and standardized neurointensive care. Concussion in chronic traumatic encephalopathy. Repeat mild traumatic brain injury: Cerebral vascular injury in traumatic brain injury.

Therapy development for diffuse axonal injury. An overview of new and novel pharmacotherapies for use in traumatic brain injury. Agoston DV, Elsayed M. Serum-based protein biomarkers in blast-induced traumatic brain injury spectrum disorder. Brain injury from explosive blast: Traumatic brain injury in modern war. Dhawan V, Degeorgia M. Neurointensive care biophysiological monitoring. Understanding the effects of concussion using Big Data. Dabek F, Caban JJ.

Leveraging Big Data to model the likelihood of developing psychological conditions after a concussion. Clinical trials in traumatic brain injury: Transforming research and clinical knowledge in traumatic brain injury pilot: Multimodality monitoring in the neurointensive care unit: Multimodal monitoring and neurocritical care bioinformatics. Informatics in neurocritical care: Progress in developing common data elements for traumatic brain injury research: Common data elements for traumatic brain injury: Biomechanics of head impacts associated with diagnosed concussion in female collegiate ice hockey players.

Association of football subconcussive head impacts with ocular near point of convergence. Consensus statement on concussion in sport: Banky J, Mccrory PR.

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Mouthguard use in Australian football. An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Instrumented mouthguard acceleration analyses for head impacts in amateur rugby union players over a season of matches. Frequency and location of head impact exposures in individual collegiate football players. Measurement of head impacts in collegiate football players: Head impact severity measures for evaluating mild traumatic brain injury risk exposure.

Concussion occurrence and knowledge in italian football soccer J.

Measuring head kinematics in football: A six degree of freedom head acceleration measurement device for use in football. Linear and angular head acceleration measurements in collegiate football. Rotational head kinematics in football impacts: Cumulative head impact burden in high school football. Head impacts during high school football: Biomechanical properties of concussions in high school football. Analysis of real-time head accelerations in collegiate football players.

Effect of repetitive sub-concussive head impacts on ocular near point of convergence. Systemic, local, and imaging biomarkers of brain injury: Neuroproteomics and systems biology-based discovery of protein biomarkers for traumatic brain injury and clinical validation. Biomarkers of primary and evolving damage in traumatic and ischemic brain injury: Biomarkers of mild traumatic brain injury in cerebrospinal fluid and blood.

Acute biomarkers of traumatic brain injury: Biofluid biomarkers of mild traumatic brain injury: Systems biology approaches for discovering biomarkers for traumatic brain injury. Deciphering complex mechanisms in neurodegenerative diseases: The challenge of mild traumatic brain injury: Blood biomarkers for brain injury: Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses.

Making big data open: The Alzheimer's Disease Neuroimaging Initiative informatics core: Impact of the Alzheimer's Disease Neuroimaging Initiative, to Neuroimaging after mild traumatic brain injury: Diffusion tensor imaging of TBI: Imaging evidence and recommendations for traumatic brain injury: Cellular biomechanics of central nervous system injury. Cellular high-energy cavitation trauma - description of a novel in vitro trauma model in three different cell types. An in vitro model of traumatic brain injury utilising two-dimensional stretch of organotypic hippocampal slice cultures.

Effect of aging on brain injury prediction in rotational head trauma— a parameter study with a rat finite element model. Combining the finite element method with structural connectome-based analysis for modeling neurotrauma: Characterization of persistent concussive syndrome using injury reconstruction and finite element modelling. Correlation between injury pattern and Finite Element analysis in biomechanical reconstructions of Traumatic Brain Injuries. Mesh smoothing algorithm applied to a finite element model of the brain for improved brain—skull interface.

A finite element study of the dynamic response of brain based on two parasagittal slice models. Biomechanics of traumatic brain injury: Biomechanics of brain tissue. Effgen GB, Morrison B. The pivotal role of astrocytes in an in vitro stroke model of the blood—brain barrier. Stretch in brain microvascular endothelial cells cEND as an in vitro traumatic brain injury model of the blood brain barrier.

Animal models of traumatic brain injury. Bench-to-bedside and bedside back to the bench; coordinating clinical and experimental traumatic brain injury studies. Agoston DV, Risling M. Where will the new drugs for traumatic brain injury treatment be coming from? Articles from Concussion are provided here courtesy of Future Science Group.

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