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@@ -49,7 +49,7 @@ To download the input csv files used to generate the output files use:
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```shell
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datalad get bulk_data
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```
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-These files have been generated from bulk csv files downloaded from the [eurostat](https://ec.europa.eu/eurostat/web/prodcom/database) website, which have been split into files for each country and for each year (see [DEVELOPING.md](DEVELOPING.md).
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+These files have been generated from bulk csv files downloaded from the [eurostat](https://ec.europa.eu/eurostat/web/prodcom/database) website, which have been split into files for each country and for each year (see [DEVELOPING.md](DEVELOPING.md)).
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The `dodo.py` script can be used to preprocess the files in `raw_data` and convert the files in the `data` and `bulk_data` folders:
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@@ -83,7 +83,7 @@ Individual files can be converted by running the `convert_data.py` script with a
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scripts/convert_data.py prodcom data/PRODCOM2016DATA.csv outputs/PRODCOM2016DATA.nt.gz
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```
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-For conversion of the example PRODCOM data files in folder ```raw_data``` the type `prodcom` should be specified. Types `prodcom_list` and `prodcom_correspondence` are also defined, along with `prodcom_bulk_sold` and `prodcom_bulk_total` (for processing bulk files in folder `bulk_data`.
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+For conversion of the example PRODCOM data files in folder ```raw_data``` the type `prodcom` should be specified. Types `prodcom_list` and `prodcom_correspondence` are also defined, along with `prodcom_bulk_sold` and `prodcom_bulk_total` (for processing bulk files in folder `bulk_data`).
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