Compiled Data Set 1 (Standard Format) and Reference File Download Link
https://eu2.contabostorage.com/00f3241116844f24b628f46d81abb929:st1/folder6/6261/1655924401_c_baikal_-_Standar_Format.xls
2026-05-30 03:42:04 - Admin
<style> body { font-family: Arial, Helvetica, sans-serif; line-height: 1.6; margin: 0; padding: 0 2rem; background-color: #f9f9f9; color: #333; } h1, h2, h3 { color: #2c3e50; margin-top: 1.5rem; } p { margin: 1rem 0; } ul, ol { margin: 1rem 0 1rem 2rem; } table { width: 100%; border-collapse: collapse; margin: 1.5rem 0; } th, td { border: 1px solid #bbb; padding: 0.5rem; text-align: left; } th { background-color: #e2e8f0; } a { color: #0066cc; text-decoration: none; } a:hover { text-decoration: underline; } .section { margin-bottom: 2rem; } </style> <header> <h1>Compiled Data Set1 (Standard Format)</h1> <p>An introductory guide that explains the purpose, structure, and typical uses of Compiled Data Set1 (CDS1) in its standard format.</p> </header> <section class="section" id="what-is-cds1"> <h2>What is Compiled Data Set1?</h2> <p>Compiled Data Set1 (often abbreviated as CDS1) is a curated collection of observational or experimental records that have been processed into a uniform, machinereadable layout. The standard format refers to a set of conventionsfield names, data types, encoding, and metadatathat ensure every file released under the CDS1 banner can be interpreted without custom parsers.</p> <p>Typical domains that rely on CDS1 include:</p> <ul> <li>Environmental monitoring (e.g., airquality stations)</li> <li>Public health surveillance (e.g., disease incidence reports)</li> <li>Economic statistics (e.g., quarterly trade balances)</li> <li>Scientific research repositories (e.g., biodiversity observations)</li> </ul> <p>Because the data are already harmonised, analysts can focus on exploration and modelling rather than on timeconsuming cleaning steps.</p> </section> <section class="section" id="standard-format-specifications"> <h2>Standard Format Specifications</h2> <p>The standard format for CDS1 follows a strict schema that is documented in a separate datadictionary file (usually a JSON or YAML document). The main elements are:</p> <h3>1. File Container</h3> <p>CDS1 files are distributed as compressed archives (<code>.zip</code> or <code>.tar.gz</code>) containing:</p> <ol> <li>A primary data file in <code>CSV</code> (commaseparated values) or <code>TSV</code> (tabseparated values) format.</li> <li>A <code>metadata.json</code> file that describes the dataset, version, source, and licensing.</li> <li>Optional ancillary files such as codebooks, dataquality reports, and readme documents.</li> </ol> <h3>2. Core Columns</h3> <p>Every record must include the following mandatory columns:</p> <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>record_id</td> <td>String (UUID)</td> <td>Globally unique identifier for the record.</td> </tr> <tr> <td>timestamp</td> <td>ISO8601 datetime (UTC)</td> <td>Exact moment the observation was recorded.</td> </tr> <tr> <td>location_id</td> <td>String</td> <td>Reference to a location table (see ancillary files).</td> </tr> <tr> <td>parameter_code</td> <td>String</td> <td>Standardised code describing the measured variable.</td> </tr> <tr> <td>value</td> <td>Float</td> <td>Numeric measurement.</td> </tr> <tr> <td>unit</td> <td>String</td> <td>International System of Units (SI) or accepted alternative.</td> </tr> <tr> <td>quality_flag</td> <td>Integer (03)</td> <td>Indicates data reliability (0 = good, 3 = suspect).</td> </tr> </tbody> </table> <h3>3. Optional Extensions</h3> <p>Datasets may add domainspecific columns, but these must be clearly documented in the <code>metadata.json</code> under the extensions section. Examples include:</p> <ul> <li><code>sample_depth</code> for marine observations.</li> <li><code>patient_age</code> for healthrelated records.</li> <li><code>currency_code</code> for economic data.</li> </ul> <h3>4. Encoding & Delimiters</h3> <p>All text files use UTF8 encoding. The primary data file must use a singlecharacter delimiter (comma or tab) and must escape any delimiter characters that appear inside a field using double quotes (CSV) or backslashes (TSV).</p> <h3>5. Data Quality Metadata</h3> <p>The <code>metadata.json</code> file contains a <code>quality_report</code> object with summary statistics, missingvalue counts, and any known sensor issues. Example excerpt:</p> <pre>{ "quality_report": { "missing_values": 124, "outliers_detected": 17, "sensor_maintenance": [ {"date": "2023-07-15", "action": "calibration"}, {"date": "2024-01-02", "action": "filter replacement"} ] }}</pre> </section> <section class="section" id="how-to-use-cds1"> <h2>How to Use CDS1 Data</h2> <p>Because the format is deterministic, most programming environments provide builtin utilities for loading the data. Below are short snippets for three popular languages.</p> <h3>Python (pandas)</h3> <pre>import pandas as pdimport jsonimport zipfilewith zipfile.ZipFile('CDS1_2024_Q1.zip') as z: with z.open('data.csv') as f: df = pd.read_csv(f) with z.open('metadata.json') as f: meta = json.load(f)print(df.head())print(meta['dataset_version'])</pre> <h3>R (readr)</h3> <pre>library(readr)library(jsonlite)unz <- unzip("CDS1_2024_Q1.zip", list = TRUE)$Namedf <- read_csv(unz[grepl("data", unz)])meta <- fromJSON(unz[grepl("metadata", unz)])head(df)meta$dataset_version</pre> <h3>JavaScript (Node.js)</h3> <pre>const fs = require('fs');const unzipper = require('unzipper');const csv = require('csv-parser');async function loadCDS1(zipPath) { const entries = await fs.createReadStream(zipPath).pipe(unzipper.Parse({forceStream: true})).promise(); const data = []; let meta = {}; for await (const entry of entries) { const name = entry.path; if (name.endsWith('.csv')) { entry.pipe(csv()).on('data', row => data.push(row)); } else if (name.endsWith('metadata.json')) { const chunks = []; entry.on('data', c => chunks.push(c)); entry.on('end', () => meta = JSON.parse(Buffer.concat(chunks).toString())); } else { entry.autodrain(); } } return {data, meta};}loadCDS1('CDS1_2024_Q1.zip').then(({data, meta}) => { console.log(data.slice(0,5)); console.log(meta.dataset_version);});</pre> <p>These examples illustrate the ease with which a researcher can bring the data into analysis pipelines.</p> </section> <section class="section" id="best-practices"> <h2>Best Practices for Working with CDS1</h2> <ol> <li><strong>Validate the schema.</strong> Before deep analysis, run a quick validation script that checks for required columns, correct data types, and acceptable ranges for <code>quality_flag</code>.</li> <li><strong>Preserve original timestamps.</strong> Convert timestamps to a timezoneaware object as early as possible; this prevents errors in timeseries aggregation.</li> <li><strong>Document any derived variables.</strong> If you calculate moving averages or normalize values, store the formulas and parameters in a separate derived_variables.json that accompanies any results you share.</li> <li><strong>Respect licensing.</strong> The metadata file specifies the datasets usage rights (e.g., CCBY4.0). Cite the dataset version and the originating agency in any publication.</li> <li><strong>Track data provenance.</strong> Keep a copy of the exact archive you used, together with its checksum (SHA256). This ensures reproducibility when others request the same version.</li> </ol> </section> <section class="section" id="common-applications"> <h2>Common Applications</h2> <p>Because CDS1 supplies clean, structured data, it is a preferred source for a wide range of analytical tasks:</p> <ul> <li><strong>TimeSeries Forecasting:</strong> Seasonal ARIMA or Prophet models built on hourly airquality readings.</li> <li><strong>Spatial Analysis:</strong> Joining <code>location_id</code> to GIS shapefiles for heatmap visualisations of disease incidence.</li> <li><strong>Machine Learning Classification:</strong> Using the <code>quality_flag</code> as a target variable to train models that predict data reliability.</li> <li><strong>Policy Impact Assessment:</strong> Comparing pre and postregulation values of emissions to quantify effectiveness.</li> </ul> </section> <section class="section" id="getting-the-data"> <h2>How to Obtain CDS1</h2> <p>The datasets are hosted on the official opendata portals of participating agencies. A typical download workflow is:</p> <ol> <li>Navigate to the portal (e.g., <a href="https://data.example.org/cds1">data.example.org/cds1</a>).</li> <li>Select the desired time range and geographic coverage.</li> <li>Choose the Standard Format ZIP option and click Download.</li> <li>Verify the integrity of the file using the provided SHA256 checksum.</li> </ol> <p>For automated pipelines, the portal offers an API endpoint that returns the latest archive URL as JSON. Sample call:</p> <pre>curl https://api.example.org/cds1/latest | jq .download_url</pre> </section> <section class="section" id="conclusion"> <h2>Conclusion</h2> <p>Compiled Data Set1 in its standard format offers a reliable, readytoanalyse foundation for many quantitative projects. By adhering to a clear schema, providing comprehensive metadata, and following bestpractice guidelines, users can minimise preprocessing effort and focus on generating insights. Whether you are a policy analyst, a data scientist, or a researcher, CDS1 serves as a trustworthy building block for reproducible and transparent work.</p> </section>```