Based on the initial content review, it looks like:
- The first file appears to be the CSV data file (`covid_jpn_total.csv`) based on its content starting with CSV headers and rows of data.
- The second file seems to be a README document based on its textual content starting with "## About Dataset".

Now that we've identified each file, I will first analyze the CSV file for the mentioned hint pertaining to non-cumulative data issues. Afterward, I will examine the README file to ensure it aligns with the dataset explanations and does not contradict or omit necessary information regarding data cumulativeness.The structure of the dataset indicates a daily record of COVID-19 data by Location ("Domestic" or "Returnee"), with various columns describing tested cases, hospitalized cases, discharged cases, fatalities, and vaccination stages. To analyze non-cumulativeness, we need to check if the data for key metrics such as 'Positive', 'Tested', 'Discharged', 'Fatal', amongst others, are cumulatively increasing over time per location category. An easy start would be to examine columns such as `Positive` and `Tested` to see if their values are increasing cumulatively for each location over the dates available.

Let's proceed with checking the cumulativeness of the 'Positive' cases for both location categories ('Domestic' and 'Returnee') over time. We will look for instances where, for the same 'Location' type, the number of 'Positive' cases decreases from one entry to the next, which would indicate a potential non-cumulativeness issue.The analysis of the COVID-19 dataset has revealed several instances where data does not appear to be cumulative, which deviates from expectations based on the hint provided. These non-cumulative instances are found across different 'Location' categories such as "Domestic," "Returnee," and "Airport." Here are the issues identified:

1. **Domestic Location Non-cumulative Data:**
   - On **2022-03-17**, the 'Positive' count decreased by **56,483** from the previous entry.
   - On **2023-03-13**, the 'Positive' count decreased by **54,532** from the previous entry.
   - On **2023-05-02**, the 'Positive' count decreased by **782** from the previous entry.

2. **Returnee Location Non-cumulative Data:**
   - On **2022-09-27**, the 'Positive' count decreased by **15** from the previous entry, even falling to **0**, which raises questions about data integrity or reporting methodology.

3. **Airport Location Non-cumulative Data:**
   - Multiple dates (**2021-05-21, 2022-03-17, 2023-01-06, 2023-03-08**) showed decreases in 'Positive' counts, with changes ranging from **-10** to **-926**.

It appears that either data entry errors, reporting methodology changes, or updates/revisions to previously reported figures have led to these anomalies. For a dataset expected to contain cumulative totals, such decreases are highly irregular and undermine the dataset's integrity for time-series analysis or trend observation.

Given these findings, it's also pertinent to review the README file to check for any explanations or disclaimers that might account for these inconsistencies.The README file provides an overview of the dataset, including context, sources, and special notes about the content. It mentions various aspects of the data, like the total number of cases in Japan, vaccination numbers, and the structure of hospitalization and symptomatology data. However, the explanation is truncated due to its length, and from the visible part, there’s no explicit mention of potential reasons for non-cumulative data entries or any disclaimers that may directly address the observed issues in the 'Positive' case counts.

Based on the visible content, there appears to be no direct acknowledgment of or guidance on handling the non-cumulativeness detected in the data, which could lead to misinterpretation of the dataset's integrity or its appropriate use in analyses. The absence of such important disclaimers can be problematic for data users who expect cumulative totals to conduct time-series analyses or observe trends reliably.

Here are the identified issues related to the dataset based on the hint and the review of the README content:

1. **Non-cumulative Data in CSV File Issue:**
   - **Evidence:** Several instances in the 'Positive' column across different 'Location' categories (Domestic, Returnee, Airport) showed reductions in case counts on subsequent dates, which contradicts the expectation of cumulatively increasing data.
   - **Description:** The dataset exhibits non-cumulative behavior in critical metrics, which might indicate data corrections, reporting errors, or methodology changes not documented within the available dataset information.

2. **Lack of Explanation for Non-cumulative Data in README:**
   - **Evidence:** The README file does not contain explicit disclaimers or explanations to address the non-cumulative data points observed in the dataset. There is no mention of potential data corrections or methodological nuances that could result in such discrepancies.
   - **Description:** The absence of clear documentation or disclaimers regarding the non-cumulative nature of some data points might lead to misinterpretation or misuse of the dataset, particularly for users relying on cumulative totals for analysis. Proper documentation is crucial to ensure the dataset's integrity and reliability for research or decision-making.

These issues suggest the need for a thorough review of the dataset's compilation and reporting processes, as well as an update to the accompanying documentation to accurately reflect the dataset's characteristics and any limitations.