datacommonsorg · gurusai-voleti · Sep 7, 2026 · Sep 7, 2026 · Sep 7, 2026
diff --git a/scripts/earthengine/README.md b/scripts/earthengine/README.md
@@ -11,7 +11,7 @@ The scripts use [Google Earth Engine](https://earthengine.google.com/)
 to extract flooded regions as a [geoTIFF](https://en.wikipedia.org/wiki/GeoTIFF)
 file.  These `.tif` files are exported to [Google Cloud
 Storage](https://cloud.google.com/storage) (GCS).
-The raster files are then copied over using `gsutil` to be processed into `.csv` locally.
+The raster files are then copied over using `gcloud storage` to be processed into `.csv` locally.
 
 
 The tools can be installed as follows:
@@ -36,7 +36,7 @@ earthengine authenticate --quiet
 ```
 
 ### Google Cloud SDK
-Install the `gsutil` command to copy files from GCS to the local
+Install the `gcloud storage` command to copy files from GCS to the local
 machine.
 
 1. Install GCS tools using the command:
@@ -55,7 +55,7 @@ gcloud auth login
 To copy files on GCS, create a storage bucket with the following command or on
 the cloud console.
 ```
-gsutil mb gs://<GCS-BUCKET-NAME>/
+gcloud storage buckets create gs://<GCS-BUCKET-NAME>/
 ```
 
 ## Extract geoTIFF from EarthEngine (EE)
@@ -99,7 +99,7 @@ in the previous step into a StatVarObservation MCF nodes for
 following:
 ```
 # Download the geoTiff from GCS
-gsutil cp gs://<GCS_BUCKET>/<GCS_FOLDER>/*.tif .
+gcloud storage cp gs://<GCS_BUCKET>/<GCS_FOLDER>/*.tif .
 # Convert the geoTiff into raster
 python3 raster_to_csv.py \
   --input_geotiff=ee_image_dynamic_world-band_water-r_max-mask_land-s_1000-from_2022-01-01.tif \

diff --git a/scripts/oecd/regional_demography/README.md b/scripts/oecd/regional_demography/README.md
@@ -39,7 +39,7 @@ In each subdirectory, the source CSVs are saved as `REGION_DEMOGR_{subdirectory
 Population raw data is stored [here](https://pantheon.corp.google.com/storage/browser/_details/datcom-source-data/oecd/regional_demography/population/REGION_DEMOGR_population.csv?authuser=0&project=datcom-204919) since it's quite big. 
 To copy raw CSV into the population directory, run:
 ```bash
-gsutil cp gs://datcom-source-data/oecd/regional_demography/population/REGION_DEMOGR_population.csv .
+gcloud storage cp gs://datcom-source-data/oecd/regional_demography/population/REGION_DEMOGR_population.csv .
 ```
 
 Population raw CSV is missing data for region_id CL16. To add the missing data, we created manual_curated_population.csv with command 

diff --git a/scripts/us_bts/latch/download_script.py b/scripts/us_bts/latch/download_script.py
@@ -120,7 +120,7 @@ def _download_paginated_socrata_file(
 def create_download_configs(gcs_urls_config_file: str) -> List[Dict]:
     """Reads the URL config JSON from GCS and generates the download configurations."""
     try:
-        result = subprocess.run(['gsutil', 'cat', gcs_urls_config_file],
+        result = subprocess.run(['gcloud', 'storage', 'cat', gcs_urls_config_file],
                                 capture_output=True,
                                 text=True,
                                 check=True,
@@ -131,7 +131,7 @@ def create_download_configs(gcs_urls_config_file: str) -> List[Dict]:
         )
     except (subprocess.CalledProcessError, FileNotFoundError) as e:
         logging.fatal(
-            f"Failed to read GCS config file '{gcs_urls_config_file}': {e}. Ensure 'gsutil' is in your PATH and the file exists."
+            f"Failed to read GCS config file '{gcs_urls_config_file}': {e}. Ensure 'gcloud' is in your PATH and the file exists."
         )
         return []
     except json.JSONDecodeError as e:

diff --git a/scripts/us_eia/opendata/generate_jsonl_for_bq.py b/scripts/us_eia/opendata/generate_jsonl_for_bq.py
@@ -19,7 +19,7 @@
 
 To import to bigquery:
 - run this script: `python3 generate_jsonl_for_bq.py`
-- copy tmp_bq_import/ to gcs: `gsutil -m cp -r bq_import gs://us_eia/`
+- copy tmp_bq_import/ to gcs: `gcloud storage cp --recursive bq_import gs://us_eia/`
 - load data into bigquery:
     ```
     bq load \

diff --git a/scripts/us_nces/README.md b/scripts/us_nces/README.md
@@ -22,13 +22,13 @@ This dataset has Population Estimates for the National Center for Education Stat
 
  Note: Give one year at a time for District and Public Schools as there are large number of column values.
 
-### upload the files manually to GCP bucket for processing using gsutil command.
+### upload the files manually to GCP bucket for processing using gcloud storage command.
     Ex private :
-        gsutil cp -r /scripts/us_nces/demographics/private_school/input_files gs://unresolved_mcf/us_nces/demographics/private_school/semi_automation_input_files/
+        gcloud storage cp --recursive /scripts/us_nces/demographics/private_school/input_files gs://unresolved_mcf/us_nces/demographics/private_school/semi_automation_input_files/
     Ex public :
-        gsutil cp -r /scripts/us_nces/demographics/public_school/input_files gs://unresolved_mcf/us_nces/demographics/public_school/semi_automation_input_files/
+        gcloud storage cp --recursive /scripts/us_nces/demographics/public_school/input_files gs://unresolved_mcf/us_nces/demographics/public_school/semi_automation_input_files/
     Ex district :
-        gsutil cp -r /scripts/us_nces/demographics/school_district/input_files gs://unresolved_mcf/us_nces/demographics/school_district/semi_automation_input_files/
+        gcloud storage cp --recursive /scripts/us_nces/demographics/school_district/input_files gs://unresolved_mcf/us_nces/demographics/school_district/semi_automation_input_files/
 
 ### Note:
     The only manual part here is after downloading the input files and then uploading them to gcp bucket. Once they're uploaded, Each import requires its own sh command to copy the files from Google Cloud to a local folder called gcs_folder/input_files. From there, a script automatically picks up these files to process them. Finally, it generates the output and saves it in gcs_folder/output_files

diff --git a/scripts/who/gho/README.md b/scripts/who/gho/README.md
@@ -42,7 +42,7 @@ The following are the files generated by the [import_data.py](import_data.py) sc
 1. Get the raw data in one of 2 ways:
     - To get the pre-downloaded data from our Google Cloud Storage, run and then unzip the file:
      ```
-     gsutil cp gs://datcom-source-data/who/gho/indicator_data.zip .
+     gcloud storage cp gs://datcom-source-data/who/gho/indicator_data.zip .
      ```
     - To download data using the WHO API (this can take about 1 hour), run:
      ```

diff --git a/scripts/world_bank/boundaries/country_boundaries_exploration.ipynb b/scripts/world_bank/boundaries/country_boundaries_exploration.ipynb
@@ -156,7 +156,7 @@
         "z = zipfile.ZipFile(io.BytesIO(r.content))\n",
         "z.extractall(TMP_PATH)\n",
         "\n",
-        "!gsutil -m cp -r {TMP_PATH} {GCS_BUCKET}"
+        "!gcloud storage cp --recursive {TMP_PATH} {GCS_BUCKET}"
       ],
       "execution_count": 16,
       "outputs": [

diff --git a/statvar_imports/brazil_visdata/brazil_rural_development_program/run.sh b/statvar_imports/brazil_visdata/brazil_rural_development_program/run.sh
@@ -5,7 +5,7 @@ SCRIPT_PATH=$(realpath "$(dirname "$0")")
 
 mkdir -p "input_files"
 
-gsutil cp "gs://unresolved_mcf/country/brazil/VISDATA/Benefits_RuralDevelopmentProgram/latest/input_files/*.csv" "$SCRIPT_PATH/input_files"
+gcloud storage cp "gs://unresolved_mcf/country/brazil/VISDATA/Benefits_RuralDevelopmentProgram/latest/input_files/*.csv" "$SCRIPT_PATH/input_files"
 
 PROCESSOR_SCRIPT="$SCRIPT_PATH/../../../tools/statvar_importer/stat_var_processor.py"
 INPUT_BASE_PATH="$SCRIPT_PATH/input_files"

diff --git a/statvar_imports/fbi/fbigovcrime/preprocess.py b/statvar_imports/fbi/fbigovcrime/preprocess.py
@@ -59,19 +59,19 @@ def copy_and_process_files_from_gcs(gcs_bucket, gcs_prefix, local_base_dir):
     logging.info(f"Listing files from: {gcs_source_path_wildcard}")
 
     try:
-        result = subprocess.run(['gsutil', 'ls', gcs_source_path_wildcard], capture_output=True, text=True, check=True)
+        result = subprocess.run(['gcloud', 'storage', 'ls', gcs_source_path_wildcard], capture_output=True, text=True, check=True)
         gcs_files = result.stdout.strip().split('\n')
         if not gcs_files or gcs_files == ['']:
             logging.fatal(f"No .xlsx files found at '{gcs_source_path_wildcard}'")
             raise RuntimeError('No .xlsx files found')
             return
     except subprocess.CalledProcessError as e:
-        logging.fatal(f"Error listing files from GCS with gsutil: {e.stderr}")
+        logging.fatal(f"Error listing files from GCS with gcloud storage: {e.stderr}")
         raise RuntimeError('Error listing files from GCS')
         return
     except FileNotFoundError:
-        logging.fatal("gsutil command not found. Please ensure the Google Cloud SDK is installed and in your PATH.")
-        raise RuntimeError('gsutil command not found')
+        logging.fatal("gcloud storage command not found. Please ensure the Google Cloud SDK is installed and in your PATH.")
+        raise RuntimeError('gcloud storage command not found')
         return
 
     logging.info(f"Found {len(gcs_files)} xlsx files to copy and process.")

diff --git a/statvar_imports/india_ndap/india_nss_health_ailments/README.md b/statvar_imports/india_ndap/india_nss_health_ailments/README.md
@@ -132,8 +132,6 @@ If the download script fails or logs HTTP `401 Unauthorized` / `403 Forbidden` e
    * Upload the updated configuration file to GCS:
      ```bash
      gcloud storage cp download_config.json gs://unresolved_mcf/india_ndap/NDAP_NSS_Health/latest/download_config.json
-     # or
-     gsutil cp download_config.json gs://unresolved_mcf/india_ndap/NDAP_NSS_Health/latest/download_config.json
      ```
 3. **Verify Download**:
    * Re-run `python3 download_script.py` and verify `india_nss_health_ailments.csv` downloads successfully.