The dataset consists of nutritional data and COVID-19 statistics by country, but there are noticeable issues considering the hint about data inconsistency and lack of information. Below are some issues identified:

1. **Lack of comprehensive documentation in README:**
   - **Evidence:** The README file starts with a motivational quote and questions about what can be done to help during the COVID-19 pandemic but does not provide detailed documentation or explanation about the dataset's columns, sources, and how it was compiled.
   - **Description:** For a dataset, especially one containing nutritional data and COVID-19 statistics, it's essential to have comprehensive documentation detailing each column, the source of data, how the data was collected, any assumptions made, and the time frame the data covers. The lack of this information could lead to misinterpretation or misuse of the dataset.

2. **Inconsistent or absent undernourishment data:**
   - **Evidence:** The 'Undernourished' column shows some countries with `NaN` values (e.g., Antigua and Barbuda).
   - **Description:** Missing or inconsistent data in the 'Undernourished' column could impact the analysis of nutritional status and its potential correlations with COVID-19 outcomes. The absence of values might indicate a lack of information or data collection issues, affecting the completeness and reliability of the dataset.

3. **Use of percentages without baseline or reference year for COVID-19 statistics:**
   - **Evidence:** The columns 'Confirmed', 'Deaths', 'Recovered', and 'Active' are represented in percentages with the note "Unit (all except Population) %", which could suggest these are percentages of the population. Still, there is no information about the time frame these percentages cover or the baseline figures they're calculated from.
   - **Description:** Without a clear reference to the time frame or specific baseline numbers, these percentages could provide a misleading picture of the COVID-19 impact on each country. The lack of temporal detail makes it difficult to use this data for temporal analyses or comparisons over time.

These issues could significantly impact the utility and interpretation of the dataset, and addressing them would improve its quality and reliability for research or policy-making purposes.