Data collected and formatted by Justin Timperio: "In my exploration of world of big data and I became curious about tick data. Tick data is extremely granular and provides a great challenge for those looking to work on their optimization skills due to its size. Unfortunately, market data is almost always behind a pay wall or de-sampled to the point of uselessness. After discovering the Dukascopy api, I knew I wanted to make this data available for all in a more accessible format." Total Line Count: 8,495,770,706 Total Data Points: 33,983,082,824 Total Decompressed Size: 501 GB

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Tick data collected by Mount Allison University including Lyme disease test results. For instructions on how to view and search this dataset there are posted resources at https://gnb.socrata.com/en/videos *This data has been generalized for privacy and is only based on ticks sent to Mount Allison University

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https://www.cmegroup.com/trading/equity-index/us-index/sandp-500_quotes_globex.html

Tick (trades only) sample data for E Mini S&P EP timestamped in Chicago time

Phylogenetic tree of tick and tick-associated bacteria in this study. We collected more than 20,000 contemporary and historical (up to 60 years of preservation) tick samples representing a wide range of tick biodiversity across diverse geographic regions in China. Metagenomic sequencing was performed on individual ticks to obtain the complete or near-complete mitochondrial (mt) genome sequences from 46 tick species, among which mitochondrial genomes were revealed for the first time for 23 species. These new mt genomes data greatly expanded the diversity of many tick groups and revealed five cryptic species. Utilizing the same metagenomic sequence data we identified divergent and abundant bacteria in Haemaphysalis, Ixodes, Dermacentor and Carios ticks, including nine species of pathogenetic bacteria and potentially new species within the genus Borrelia. We also used these data to explore the evolutionary relationship between ticks and their associated bacteria, revealing a pattern of long-term co-divergence relationship between ticks and Rickettsia and Coxiella bacteria.

Browse Silver Futures (SI) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Browse E-mini S&P 500 Futures (ES) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Tick (Bids | Asks | Trades | Settle) sample data for E Mini S&P EP timestamped in Chicago time

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, behavior, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers interested in enumerating questing ticks attempt to integrate this heterogeneity by conducting replicate sampling bouts spread over the tick questing period as common field methods notoriously underestimate ticks. However, it is unclear how (or if) tick studies account for this heterogeneity in the modeling process. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1%) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data. Methods Methods To illustrate the problems that arise from not accounting for the detection process while estimating tick abundance, we performed two simulations that mirrored tick dragging studies and used common statistical frameworks for modeling tick data. For both simulations, we chose 5 temporal replicate surveys of 100 plots and specified a positive relationship of temperature on abundance and detection probability; average abundance (λ) was arbitrarily set to 20 ticks. Our choice of replicate surveys is a common field design for studying ticks (Dobson, 2013), and environmental factors such as temperature influence tick abundance and activity (Gilbert, 2021; Klarenberg and Wisely, 2019) and are often used to model tick abundance. For our first simulation, we specified low detection probability (ρ = 0.2) as tick dragging surveys often only collect ~10–20% of questing ticks (Drew and Samuel, 1985; Nyrhilä et al., 2020). We assumed perfect detection (ρ = 1) for our second simulation, meaning that all ticks would be captured by dragging or flagging surveys if they were present. We simulated count data arising from a negative binomial distribution using the 'simNmix' function from AHMbook R package (Kéry et al., 2022) as tick abundance data often have a high variance-to-mean ratio due to aggregated and high counts (Elston et al., 2001). Following simulations, we estimated abundance and evaluated relationships between average seasonal temperature and tick abundance using 3 common approaches for modeling tick count data (linear models [LM], generalized linear models [GLM], and generalized linear mixed-effects models [GLMM]). For the LM analysis, we added 1 to the tick counts and log-transformed counts to meet assumptions of normality. This approach, although problematic, is a standard method to force count data into a linear modeling framework (O’Hara and Kotze, 2010) and common in tick studies (e.g., Allen et al., 2019). For the GLM and GLMM analyses, we used the raw counts and specified models with negative binomial errors. We used the ‘lm’ and ‘glm’ functions in the base R package for the LM and GLM analyses, respectively, and the ‘glmmTMB’ function in the glmmTMB R package (Brooks et al., 2017) for the GLMM analysis. To highlight the shortcomings of the preceding analytical approaches, we compared inference with an N-mixture or binomial mixture model – a type of HM that is often used for estimating abundance when count data are imperfectly detected (Kéry and Royle, 2015). We fit the N-mixture model using the ‘pcount’ function in the unmarked R package (Fiske and Chandler, 2011) and specified average temperature across all sampling occasions as a covariate on abundance (λ) and temperature during each sampling occasion as a covariate on detection probability (ρ). We then evaluated how well each statistical approach recovered average abundance (λ = 20) and relationships between average seasonal temperature and abundance when detection is assumed to be imperfect (ρ = 0.2) and perfect (ρ = 1). All simulations and statistical analyses were performed using R software (R Core Team, 2022), and predictive plots were created using the ggplot2 R package (Wickham, 2016). References Allen, D., Borgmann-Winter, B., Bashor, L., Ward, J., 2019. The density of the Lyme disease vector Ixodes scapularis (blacklegged tick) differs between the Champlain Valley and Green Mountains, Vermont. Northeast. Nat. 26, 545–560. https://doi.org/10.1656/045.026.0307 Brooks, M.E., Kristensen, K., van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Mächler, M., Bolker, B.M., 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400. https://doi.org/10.32614/rj-2017-066 Dobson, A.D.M., 2013. Ticks in the wrong boxes: Assessing error in blanket-drag studies due to occasional sampling. Parasites and Vectors 6, 1–6. https://doi.org/10.1186/1756-3305-6-344 Drew, M.L., Samuel, W.M., 1985. Factors affecting transmission of larval winter ticks, Dermacentor albipictus (Packard), to moose, Alces alces L., in Alberta, Canada. J. Wildl. Dis. 21, 274–282. https://doi.org/10.7589/0090-3558-21.3.274 Elston, D.A., Moss, R., Boulinier, T., Arrowsmith, C., Lambin, X., 2001. Analysis of aggregation, a worked example: Numbers of ticks on red grouse chicks. Parasitology 122, 563–569. https://doi.org/10.1017/S0031182001007740 Gilbert, L., 2021. The impacts of climate change on ticks and tick-borne disease risk. Annu. Rev. Entomol. 66, 273–288. https://doi.org/10.1146/annurev-ento-052720-094533 Kéry, Marc, Royle, J.A., Meredith, M., 2022. AHMbook: Functions and Data for the Book “Applied Hierarchical Modeling in Ecology” Vols 1 and 2. Kéry, M., Royle, J.A., 2015. Applied hierarchical modeling in ecology: Analysis of distribution, abundance, and species richness in R an BUGS. Academic Press. https://doi.org/https://doi.org/10.1016/C2015-0-04070-9 Klarenberg, G., Wisely, S.M., 2019. Evaluation of NEON data to model spatio-temporal tick dynamics in Florida. Insects 10. https://doi.org/10.3390/insects10100321 Nyrhilä, S., Sormunen, J.J., Mäkelä, S., Sippola, E., Vesterinen, E.J., Klemola, T., 2020. One out of ten: low sampling efficiency of cloth dragging challenges abundance estimates of questing ticks. Exp. Appl. Acarol. 82, 571–585. https://doi.org/10.1007/s10493-020-00564-5 O’Hara, R.B., Kotze, D.J., 2010. Do not log-transform count data. Methods Ecol. Evol. 1, 118–122. https://doi.org/10.1111/j.2041-210x.2010.00021.x R Core Team, 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. R Found. Stat. Comput. Vienna, Austria. URL http//www.R-project.org/. Wickham, H., 2016. ggplot2: Elegant graphics for data analysis, First Ed. ed. Springer, New York, NY. https://doi.org/https://doi.org/10.1007/978-0-387-98141-3

This data product contains the quality-controlled, native sampling resolution data from NEON’s Tick and Tick-Borne Pathogen sampling crosswalked to Darwin Core. NEON tick sampling targets hard ticks in the family Ixodidae. The dataset includes collection, identification and pathogen testing data from 2014 – 2020. Data are collected across the United States including Alaska and Puerto Rico, but excluding Hawaii. Tick abundance and diversity are sampled at regular intervals using drag or flag sampling techniques. Collected ticks are identified to species and lifestage and/or sex by a professional taxonomist. A subset of identified nymphal ticks are tested for the presence of bacterial and protozoan pathogens. For additional details, see https://data.neonscience.org/data-products/DP1.10093.001 and https://data.neonscience.org/data-products/DP1.10092.001.

Browse Bitwise Bitcoin ETF (BITB) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Tick data collected by Mount Allison University including Lyme disease test results. *This data has been generalized for privacy and is only based on ticks sent to Mount Allison University

Tick data collected by Mount Allison University including Lyme disease test results. For instructions on how to view and search this dataset there are posted resources at https://gnb.socrata.com/en/videos *This data has been generalized for privacy and is only based on ticks sent to Mount Allison University

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These are the datasets underlying the figures in the manuscript "Methods of active surveillance for hard ticks and associated tick-borne pathogens of public health importance in the contiguous United States: A Comprehensive Systematic Review". The review considered only publications reporting on active tick or tick-borne pathogen surveillance in the contiguous United States published between 1944 and 2018. For the purposes of this review, we were only concerned with studies of Ixodidae (hard ticks) and/or studies of tick-borne pathogens (in humans, animals, or hard ticks) of public health importance to humans. Study designs included cross-sectional, serological, epidemiological, ecological, or observational studies. Only peer-reviewed publications published in the English language were included. Studies were excluded if they focused on a tick that is not a vector of a human pathogen or on a pathogen that does not cause disease in humans, if the tick or tick-borne pathogen findings were incidental, or if they did not include quantitative surveillance data. For the purpose of this study, we defined surveillance data as information on ticks or pathogens provided through active sampling in natural areas; it should be noted that this does not match the strict definition used by the CDC, which requires sustained sampling efforts across time. Studies were also excluded if they: explored regions other than the contiguous US; focused on treatment, vaccine, or therapeutics development and/or diagnostics of human disease; focused on tick or pathogen genetics; focused on experimental studies with ticks or hosts; were tick control and/or management studies; performed only passive surveillance; were review articles; were not peer reviewed; were in a language other than English; the full text was not available; and if the disease was not a risk to the general public. In addition, for articles which reported data that had previously been published, we only included previously unreported information collected by the authors, and we referenced the specific period of collection for these data to ensure we were not double-recording data. Due to publication delays, we also performed a non-systematic review of the literature of articles published between 2019 – 2023 on tick and tickborne pathogen surveillance methods conducted in the contiguous United States. Keyword search was performed in PubMed Central and Web of Science Core Collection databases. The search algorithm keywords included tick(s), Amblyomma, Dermacentor, Ixodes, Rhipicephalus, Acari Ixodidea, tick host(s), Lyme disease, Rocky Mountain Spotted Fever, Spotted Fever Group, Rickettsiosis, Ehrlichiosis, Anaplasmosis, Borreliosis, Tularemia, Babesiosis, tick-borne pathogen, Powassan, Heartland, Bourbon, Colorado tick fever, Pacific Coast tick fever, tick surveillance, surveillance, (sero)epidemiology, prevalence, distribution, ecology, United States. The search algorithm utilized is provided as follows: TI= ((ticks OR Ixodes OR Amblyomma OR Dermacentor OR Rhipicephalus OR "Acari Ixodidi" OR "tick hosts" OR "tick host") OR ("Lyme Disease" OR "Rocky Mountain Spotted Fever" OR "Spotted Fever Group" OR Rickettsiosis OR Rickettsial OR Ehrlichiosis OR Anaplasmosis OR Borreliosis OR Tularemia OR Babesiosis OR Borrelia OR Ehrlichia OR Anaplasma OR Rickettsia OR Babesia OR "tick-borne pathogen" OR "tick borne pathogen")) AND TS= ("tick surveillance" OR surveillance OR epidemiology OR seroepidemiology OR ecology) AND CU=("United States of America" OR "USA" OR "United States" OR United-States). These datasets are the collated data underlying the figures in the manuscript. For more details, please see the publication. The following are explanations for variables used in all the CSV files: Tick: Species of tick collected Tick_Method: Method of collecting ticks Pathogen: Species of pathogen tested for Path_Method: Method of testing for pathogens Decade: Decade of publication n: Number of publications STATE: state in which study was conducted COUNTY: county in which study was conducted 1944 - 2018 (Was surveillance performed?): was there at least one publication included with a publication date within the 1944-2018 period in this geographic region? 2019 - 2023 (Was surveillance performed?): was there at least one publication included with a publication date within the 2019-2023 period in this geographic region?

Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022.

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Nasdaq TotalView-ITCH is a proprietary data feed that disseminates full order book depth and last sale data from the Nasdaq stock market (XNAS). It delivers every quote and order at each price level, along with any event that updates the order book after an order is placed, such as trade executions, modifications, or cancellations. Nasdaq is the most active US equity exchange by volume and represented 13.03% of the average daily volume (ADV) as of January 2025.

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Breadth of coverage: 20,329 products

Asset class(es): Equities

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Browse SPDR S&P 500 ETF Trust (SPY) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

There are six diferent kinds of widgets we have;

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Single Ticker - It's a simple one-symbol sized ticker.

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Historical Chart - You can view the historical data details for a single symbol with the Historical Chart Widget.