Fundamental Data Science Large Growth ETF Investment Strategy - Fundamental Data Science Large Growth ETF	Jun. 30, 2026
Prospectus [Line Items]
Strategy [Heading]	<span style="font-family:Arial Narrow;font-size:11pt;font-weight:bold;">What are the Fund’s main investment strategies?</span>
Strategy Narrative [Text Block]	Under normal circumstances, the Fund invests at least 80% of its Assets in equity investments of large capitalization growth companies.The Fund generally defines large capitalization companies as those with a market capitalization similar to those within the universe of the Russell 1000® Growth Index at the time of purchase. As of September 30, 2026, the market capitalizations of the companies in the Russell 1000® Growth Index ranged from $2.87 billion to $5.50 trillion. Growth companies are those that the adviser believes have growth potential compared to companies in the U.S. market more broadly, based on one or more of the following factors: projected future revenue growth, projected future earnings growth, historical revenue growth or historical earnings growth. “Assets” means net assets, plus the amount of borrowings for investment purposes. In addition to direct investments in securities, derivatives, which are instruments that have a value based on another instrument, exchange rate or index, may be used as substitutes for securities in which the Fund can invest. The Fund may use futures contracts to gain or reduce exposure to its index, maintain liquidity and minimize transaction costs. In managing cash flows, the Fund buys futures contracts to invest incoming cash in the market or sells futures contracts in response to cash outflows, thereby gaining market exposure to the index while maintaining a cash balance for liquidity. Investment Process: In managing the Fund, the adviser employs a data science enabled investment approach that combines research, data insights, and risk management. The adviser defines data science as the discipline of extracting useful insights from collections of information, and the adviser utilizes the insights as part of its investment process. The adviser also utilizes proprietary techniques to process, analyze, and combine a wide variety of data sources, including the adviser’s multi-decade history of proprietary fundamental research, company financial statements, and a variety of other data sources that the adviser finds relevant to forecasting the financial prospects of each security, also known as fundamental analysis. The adviser combines insights derived from these sources to conduct fundamental analysis. Alongside its own insights, the Fund’s portfolio management team uses the forecasts developed through data science techniques to help to identify securities with attractive valuations that are priced favorably relative to their associated levels of risk. The Fund’s portfolio management team then constructs a portfolio that seeks to maximize expected future financial performance while controlling for key risks to the underlying companies’ businesses identified by the adviser’s analysis. The adviser assesses key risks by analyzing potential events or conditions that may have a negative impact on the adviser’s valuation of a particular security. Such key risks may include, but are not limited to, sensitivity to changes in macroeconomic conditions, competitive risks from existing companies or new entrants, and operational risks related to the companies’ business models. The adviser regularly evaluates the efficacy of the sources of information included within the investment process, and seeks to identify new data sources that will be additive to the adviser’s forecasts and portfolio construction, assessing the validity of its models and assumptions as new information becomes available and market conditions change. As part of its investment process, the adviser seeks to assess the impact of environmental, social and governance (ESG) factors on many issuers in the universe in which the Fund may invest. The adviser’s assessment is based on an analysis of key opportunities and risks across industries to seek to identify financially material issues with respect to the Fund’s investments in issuers and ascertain key issues that merit engagement with issuers. These assessments may not be conclusive, and securities of issuers that may be negatively impacted by such factors may be purchased and retained by the Fund while the Fund may divest or not invest in securities of issuers that may be positively impacted by such factors. The Fund is non-diversified and may invest a greater percentage of its assets in a particular issuer or group of issuers than a diversified fund would. The Fund will sell a stock if the adviser determines that the issuer no longer meets the Fund’s investment criteria or if the adviser believes that more attractive opportunities are available.

Fundamental Data Science Large Growth ETF Investment Strategy - Fundamental Data Science Large Growth ETF

Jun. 30, 2026

<span style="font-family:Arial Narrow;font-size:11pt;font-weight:bold;">What are the Fund’s main investment strategies?</span>

Strategy Narrative [Text Block]

Under normal circumstances, the Fund invests at least 80% of its Assets in equity investments of large capitalization growth companies.The Fund generally defines large capitalization companies as those with a market capitalization similar to those within the universe of the Russell 1000® Growth Index at the time of purchase. As of September 30, 2026, the market capitalizations of the companies in the Russell 1000® Growth Index ranged from $2.87 billion to $5.50 trillion. Growth companies are those that the adviser believes have growth potential compared to companies in the U.S. market more broadly, based on one or more of the following factors: projected future revenue growth, projected future earnings growth, historical revenue growth or historical earnings growth. “Assets” means net assets, plus the amount of borrowings for investment purposes. In addition to direct investments in securities, derivatives, which are instruments that have a value based on another instrument, exchange rate or index, may be used as substitutes for securities in which the Fund can invest. The Fund may use futures contracts to gain or reduce exposure to its index, maintain liquidity and minimize transaction costs. In managing cash flows, the Fund buys futures contracts to invest incoming cash in the market or sells futures contracts in response to cash outflows, thereby gaining market exposure to the index while maintaining a cash balance for liquidity. Investment Process: In managing the Fund, the adviser employs a data science enabled investment approach that combines research, data insights, and risk management. The adviser defines data science as the discipline of extracting useful insights from collections of information, and the adviser utilizes the insights as part of its investment process. The adviser also utilizes proprietary techniques to process, analyze, and combine a wide variety of data sources, including the adviser’s multi-decade history of proprietary fundamental research, company financial statements, and a variety of other data sources that the adviser finds relevant to forecasting the financial prospects of each security, also known as fundamental analysis. The adviser combines insights derived from these sources to conduct fundamental analysis. Alongside its own insights, the Fund’s portfolio management team uses the forecasts developed through data science techniques to help to identify securities with attractive valuations that are priced favorably relative to their associated levels of risk. The Fund’s portfolio management team then constructs a portfolio that seeks to maximize expected future financial performance while controlling for key risks to the underlying companies’ businesses identified by the adviser’s analysis. The adviser assesses key risks by analyzing potential events or conditions that may have a negative impact on the adviser’s valuation of a particular security. Such key risks may include, but are not limited to, sensitivity to changes in macroeconomic conditions, competitive risks from existing companies or new entrants, and operational risks related to the companies’ business models. The adviser regularly evaluates the efficacy of the sources of information included within the investment process, and seeks to identify new data sources that will be additive to the adviser’s forecasts and portfolio construction, assessing the validity of its models and assumptions as new information becomes available and market conditions change. As part of its investment process, the adviser seeks to assess the impact of environmental, social and governance (ESG) factors on many issuers in the universe in which the Fund may invest. The adviser’s assessment is based on an analysis of key opportunities and risks across industries to seek to identify financially material issues with respect to the Fund’s investments in issuers and ascertain key issues that merit engagement with issuers. These assessments may not be conclusive, and securities of issuers that may be negatively impacted by such factors may be purchased and retained by the Fund while the Fund may divest or not invest in securities of issuers that may be positively impacted by such factors. The Fund is non-diversified and may invest a greater percentage of its assets in a particular issuer or group of issuers than a diversified fund would. The Fund will sell a stock if the adviser determines that the issuer no longer meets the Fund’s investment criteria or if the adviser believes that more attractive opportunities are available.