Tweak some language in teh dataset card.

README.md

@@ -19,11 +19,14 @@ tags:
 license: cc-by-sa-4.0
 ---
 
-# Dataset Card for BOOM Benchmark
+# Dataset Card for BOOM (Benchmark of Observability Metrics)
 
 ## Dataset Summary
 
-The **BOOM Benchmark** is a large-scale, real-world time series dataset designed for evaluating models on **forecasting** tasks in complex observability environments. Collected from a high-volume telemetry system, the benchmark captures the irregularity, structural complexity, and heavy-tailed statistics typical of production observability data. Unlike synthetic or curated benchmarks, BOOM reflects the full diversity and unpredictability of operational signals observed in distributed systems, covering infrastructure, networking, databases, security, and application-level metrics.
+**BOOM** (**B**enchmark **o**f **O**bservability **M**etrics) is a large-scale, real-world time series dataset designed for evaluating models on forecasting tasks in complex observability environments.
+Composed of real-world metrics data collected from Datadog, a leading observability platform, the benchmark captures the irregularity, structural complexity, and heavy-tailed statistics typical of production observability data. Unlike synthetic or curated benchmarks, BOOM reflects the full diversity and unpredictability of operational signals observed in distributed systems, covering infrastructure, networking, databases, security, and application-level metrics.
+
+Note: the metrics comprising BOOM were generated from internal monitoring of pre-production environments, and **do not** include any customer data. 
 
 ![Dataset](intro_figure_boom.png)
 *<center>Figure 1: (a) Boom is comprised of observability time series data with distinct semantic categories corresponding to various temporal patterns; percentages indicate proportion of each category in Boom.
@@ -31,7 +34,8 @@ The **BOOM Benchmark** is a large-scale, real-world time series dataset designed
    (c) Boom is comprised of data from various system domains. </center>*
 
 
-Boom consists of 350 million points across 32887 variates. The dataset is split on 2807 dataset, each represents a metric query with one or multiple variates. These series vary widely in sampling frequency, temporal length, and number of variates. Looking beyond the basic characteristics of the series, we highlight a few of the typical challenging properties of observability time series (several of which are illustrated in Figure 1):
+Boom consists of approximately 350 million time-series points across 32,887 variates. The dataset is split into 2,807 individual time series with one or multiple variates. Each represents a metric query extracted from user-generated dashboards, notebooks, and monitors.
+These series vary widely in sampling frequency, temporal length, and number of variates. Looking beyond the basic characteristics of the series, we highlight a few of the typical challenging properties of observability time series (several of which are illustrated in Figure 1):
 
 - Zero-inflation: Many metrics track infrequent events (e.g., system errors), resulting in sparse series dominated by zeros with rare, informative spikes.
 
@@ -63,7 +67,7 @@ Each entry in the dataset consists of:
 
 ## Collection and Sources
 
-The dataset is sourced from a proprietary staging environment of an observability platform. Data was collected using a standardized query API and preprocessed to remove sensitive or production-specific information. Metric domains and structures mirror real-world telemetry from distributed systems. Each time series in Boom dataset undergoes a standardized preprocessing pipeline designed to address the noise, irregularities, and high cardinality typical of observability data.
+The data is sourced from an internal Datadog deployment monitoring pre-production systems and was collected using a standardized query API. The data undewent a basic preprocessing pipeline to remove constant or empty series, and to impute missing values.
 
 ## Comparison with Other Benchmarks
 
@@ -88,9 +92,5 @@ Links:
 ## Citation
 
 ```bibtex
-@inproceedings{boom_ds,
-  title={BOOM: },
-  author={Names,
-  year={2025},
-  booktitle={NeurIPS Time Series Workshop},
-}
+TODO
+```