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

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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Presence/absence of a pathogen in each single tick sample

Tick (trades only) 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 whi...

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

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.

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📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Coverage of 90%+ global trading volume

🔧 Technical Excellence: ◦ 99,9% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

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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 / Données relatives aux tiques recueillies par l’Université Mount Allison, y compris résultats des tests de dépistage de la maladie de Lyme. Pour obtenir des instructions sur la façon d’afficher et de rechercher cette base de données, des ressources sont disponibles à l’adresse suivante, https://gnb.socrata.com/en/videos *Ces données ont été généralisées pour des raisons de confidentialité et sont basées uniquement sur les tiques envoyés à 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

[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ] Data sources. Time series data on cattle fever tick incidence, 1959-2020, and climate variables January 1950 through December 2020, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year, October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on monthly new detections of Rhipicephalus microplus and R. annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from the Cattle Fever Tick Eradication Program (CFTEP) maintained jointly by the United States Department of Agriculture (USDA), Animal Plant Health Inspection Service and the USDA Animal Research Service in Laredo, Texas. Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the PQZ are in the main text, and in the Supporting Information (SI) of the associated paper. Data sources on oceanic indicators, on local meteorology, and their pretreatment are detailed in SI. Data pretreatment. To address the low signal-to-noise ratio and non-independence of observations common in time series, we transformed all explanatory and response variables by using a series of six consecutive steps: (i) First differences (year y minus year y-1) were calculated, (ii) these were then converted to z scores (z = (x- μ) / σ, where x is the raw value, μ is the population mean, σ is the standard deviation of the population), (iii) linear regression was applied to remove any directional trends, (iv) moving averages (typically 11-year point-centered moving averages) were calculated for each variable, (v) a lag was applied if/when deemed necessary, and (vi) statistics calculated (r, n, df, P<, p<). Principal component analysis (PCA). A matrix of z-score first differences of the 13 climate variables, and CFT (1960-2020), was entered into XLSTAT principal components analysis routine; we used Pearson correlation of the 14 x 60 matrix, and Varimax rotation of the first two components. Autoregressive Integrated Moving Average (ARIMA). An ARIMA (2,0,0) model was selected among 7 test models in which the p, d, and q terms were varied, and selection made on the basis of lowest RMSE and AIC statistics, and reduction of partial autocorrelation outcomes. A best model linear regression of CFT values on ARIMA-predicted CFT was developed using XLSTAT linear regression software with the objective of examining statistical properties (r, n, df, P<, p<), including the Durbin-Watson index of order-1 autocorrelation, and Cook’s Di distance index. Cross-validation of the model was made by withholding the last 30, and then the first 30 observations in a pair of regressions. Forecast of the next major CFT outbreak. It is generally recognized that the onset year of the first major CFT outbreak was not 1959, but may have occurred earlier in the decade. We postulated the actual underlying pattern is fully 44 years from the start to the end of a CFT cycle linked to external climatic drivers. (SI Appendix, Hypothesis on CFT cycles). The hypothetical reconstruction was projected one full CFT cycle into the future. To substantiate the projected trend, we generated a power spectrum analysis based on 1-year values of the 1959-2020 CFT dataset using SYSTAT AutoSignal software. The outcome included a forecast to 2100; this was compared to the hypothetical reconstruction and projection. Any differences were noted, and the start and end dates of the next major CFT outbreak identified. Resources in this dataset: Resource Title: CFT and climate data. File Name: climate-cft-data2.csv Resource Description: Main dataset; see data dictionary for information on each column Resource Title: Data dictionary (metadata). File Name: climate-cft-metadata2.csv Resource Description: Information on variables and their origin Resource Title: fitted models. File Name: climate-cft-models2.xlsx Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel; XLSTAT,url: https://www.xlstat.com/en/; SYStat Autosignal,url: https://www.systat.com/products/AutoSignal/

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

Three tabular datasets providing tick counts from various transects and reproduction sites around Jackson, Wyoming. One table with data from total number of winter ticks collected running a transect along either an elk or moose hide, another table with winter tick counts collected and counted in a 250 m drag transect, and then the last table is the count of larvae that come from one fed female tick.

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 / Données relatives aux tiques recueillies par l’Université Mount Allison, y compris résultats des tests de dépistage de la maladie de Lyme. Pour obtenir des instructions sur la façon d’afficher et de rechercher cette base de données, des ressources sont disponibles à l’adresse suivante, https://gnb.socrata.com/en/videos *Ces données ont été généralisées pour des raisons de confidentialité et sont basées uniquement sur les tiques envoyés à Mount Allison University.

Presence/absence of tick-borne diseases in each single rodent sample from 2020-onward. Prior to 2020, the protocol was used to detect hantavirus; these data are available in the Rodent-borne pathogen status data product (DP1.10064.001).

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 / Données relatives aux tiques recueillies par l’Université Mount Allison, y compris résultats des tests de dépistage de la maladie de Lyme. *Ces données ont été généralisées pour des raisons de confidentialité et sont basées uniquement sur les tiques envoyés à l’Université Mount Allison.

This dataset provides the results from collecting and testing nymph deer ticks, also known as blacklegged ticks, or by their scientific name Ixodes scapularis. Collection and testing take place across New York State (excluding New York City) from May to September, when nymph deer ticks are most commonly seen.

Nymph deer ticks are individually tested for different bacteria and parasites, which includes the bacteria responsible for Lyme disease. These data should simply be used to educate people that there is a risk of coming in contact with ticks and tick-borne diseases.

These data only provide nymph tick infections at a precise location and at one point in time. Both measures, tick population density and percentage, of ticks infected with the specified bacteria or parasite can vary greatly within a very small area and within a county. These data should not be used to broadly predict disease risk for a county.

Further below on this page you can find links to tick prevention tips, a video on how to safely remove a tick, and more datasets with tick testing results. Interactive charts and maps provide an easier way to view the data.

Data for this study were obtained through a partnership with the USDA-FIA. Ticks were collected voluntarily according to methods outlined in Trout Fryxell and Vogt 2019 by FIA foresters conducting standard inventory operations over a period of five years (2017–2021) in the southeastern U.S. Vegetation data were collected by USDA-FIA crews on permanent ground sampling plots located across the study area at a sampling intensity of 1 plot per 2,428 ha. Crews indicated where ticks were encountered and those plots were cross-referenced with the USDA FIA database.

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 / Données relatives aux tiques recueillies par l’Université Mount Allison, y compris résultats des tests de dépistage de la maladie de Lyme. Pour obtenir des instructions sur la façon d’afficher et de rechercher cette base de données, des ressources sont disponibles à l’adresse suivante, https://gnb.socrata.com/en/videos *Ces données ont été généralisées pour des raisons de confidentialité et sont basées uniquement sur les tiques envoyés à Mount Allison University.