fix typos, small tweaks

docs/source/roadmap.md

@@ -2,7 +2,7 @@
 
 
 ## Scope and goals
-`Echopype` is a library aimed at enabling interoperability and scalability in processing ocean sonar data. The current focus and scope are on scientific echosounders widely used in fisheries and marine ecological surveys. We envision that Echopype will provide "building blocks" that can be strung together to construct data processing pipelines to bring raw data files collected by these instruments to "analysis-ready" data products that can be used by researchers in the ocean sciences community. We also plan for Echopype to be flexible in accommodating both local and cloud computing environments, such that data processing pipelines can be prototyped locally and scaled up for larger scale processing on the cloud.
+Echopype is a library aimed at enabling interoperability and scalability in processing ocean sonar data. The current focus and scope are on scientific echosounders widely used in fisheries and marine ecological surveys. We envision that Echopype will provide "building blocks" that can be strung together to construct data processing pipelines to bring raw data files collected by these instruments to "analysis-ready" data products that can be used by researchers in the ocean sciences community. We also plan for Echopype to be flexible in accommodating both local and cloud computing environments, such that data processing pipelines can be prototyped locally and scaled up for larger scale processing on the cloud.
 
 To achieve these goals, we develop a workflow that focuses first on standardizing data to the widely supported netCDF data model, and based on the standardized data build computation and visualization routines by leveraging open-source libraries in the scientific Python software ecosystem, especially those in [the Pandata stack](https://github.com/panstacks/pandata?tab=readme-ov-file).
 
@@ -12,13 +12,13 @@ To achieve these goals, we develop a workflow that focuses first on standardizin
 
 ## Data standardization
 
-At the core of `echopype` is a data conversion and standardization module that returns an `EchoData` object that allows easy and intuitive access and understanding of echosounder data in a netCDF data model. Currently echopype converts raw instrument data files into netCDF4 or Zarr files following a modified version of the [ICES SONAR-netCDF4 convention](https://github.com/ices-publications/SONAR-netCDF4/) that we believe improves the data coherence and efficiency of data access (see [details here](./data-format-sonarnetcdf4)). This conversion step ensures that downstream processing can be developed and executed in an instrument-agnostic manner, which is critical for tackling the tedious and labor-intensive data wrangling operations.
+Echopype contains a core data conversion and standardization subpackage that returns an `EchoData` object that allows easy and intuitive access and understanding of echosounder data in a netCDF data model. Currently Echopype converts raw instrument data files into netCDF4 or Zarr files following a modified version of the [ICES SONAR-netCDF4 convention](https://github.com/ices-publications/SONAR-netCDF4/) that we believe improves the data coherence and efficiency of data access (see [details here](./data-format-sonarnetcdf4)). This conversion step ensures that downstream processing can be developed and executed in an instrument-agnostic manner, which is critical for tackling the tedious and labor-intensive data wrangling operations.
 
 As the core data representation stabilizes across [a few echosounder models](https://echopype.readthedocs.io/en/stable/convert.html#supported-raw-file-types), going forward we plan to:
 - Continue maintaining and updating the standardized `EchoData` object in accordance with the evolution of community raw data conventions
 - Enhance adherence to community conventions of metadata and processed data, such as the [ICES AcMeta](https://github.com/ices-publications/AcMeta), the new Gridded group introduced in SONAR-netCDF4 v2.0, and the [Australia IMOS SOOP-BA conventions](https://imos.org.au/fileadmin/user_upload/shared/SOOP/BASOOP/SOOP-BA_NetCDF_Conventions_Version_2.2.pdf)
 - Add support for data from other echosounder models, including historical data from Simrad EK/BI500 and the Biosonics [DT4 files](https://www.biosonicsinc.com/download/dt4-file-format-specification/)
-- Develop converter between data from major versions of echopype
+- Develop converter between data from major versions of Eechopype
 
 
 
@@ -28,7 +28,7 @@ In parallel with code development, the Echopype team is also working on defining
 
 In Echopype, currently many functions generate prototype [data provenance](https://eos.org/opinions/the-importance-of-data-set-provenance-for-science) and processing level information as data variables or attributes. Going forward we plan to:
 - Collaborate with the echosounder community to further refine the definitions for echosounder data processing levels
-- Improve data provenance preservation along the "chain" of data conversio and processing functions
+- Improve data provenance preservation along the "chain" of data conversion and processing functions
 
 
 
@@ -43,15 +43,15 @@ As the foundational data standardization components of Echopype mature, in the n
 
 ## Computational scalability
 
-Computational scalability is a core goal of `echopype` development. We aim to provide scalable data processing capability for researchers both on their own personal computers and on cloud or on-premise computing clusters. The Echopype data conversion tools provide direct read/write interface with both local filesystems and cloud storage, and all downstream data processing functions also natively interface with both local and cloud resources through the combination of the Zarr, Xarray, Dask, and related libraries. However, we have found that the often irregular spacing and structure of echosounder data in time and space can impose substantial computational bottleneck and require custom optimization beyond stock Xarray functions to parallelize efficiently across computing agents. With a few important memory issues during data conversion resolved (see [v0.8.0 release notes](https://echopype.readthedocs.io/en/stable/whats-new.html#v0-8-0-2023-august-27)), going forward we will:
+Computational scalability is a core goal of Echopype development. We aim to provide scalable data processing capability for researchers both on their own personal computer and on computing clusters. The Echopype data conversion tools provide direct read/write interface with both local filesystems and cloud storage, and all downstream data processing functions also natively interface with both local and cloud resources through the combination of the Zarr, Xarray, Dask, and related libraries. However, we have found that the often irregular spacing and structure of echosounder data in time and space can impose substantial computational bottleneck and require custom optimization beyond stock Xarray functions to parallelize efficiently across computing agents. With a few important memory issues during data conversion resolved (see [v0.8.0 release notes](https://echopype.readthedocs.io/en/stable/whats-new.html#v0-8-0-2023-august-27)), going forward we plan to:
 - Benchmark data processing functions against diverse datasets of different volume (100s of GB to TB) and spatiotemporal features that can cause unintended memory expansion during computation
 - Leverage Dask delayed approaches and experiment with different Zarr chunking schemes to resolve computational bottlenecks
 
 
 
 ## Companion developments
 
-Echopype focuses on data standardization, aggregations, and processing for building efficient and scalable data workflow. To cover other needs in integrative analysis of echosounder data, the Echopype development team is taking a modularized approach to develop the following companion libraries:
+Echopype focuses on data standardization, aggregations, and processing for building efficient and scalable data workflow. To address other needs in integrative analysis of echosounder data, the Echopype team is taking a modularized approach to develop the following companion libraries:
 
 - [Echoflow](https://github.com/OSOceanAcoustics/echoflow): Orchestrate workflow on the cloud or local platforms
 - [Echoshader](https://github.com/OSOceanAcoustics/echoshader): Interactive visualization widgets leveraging the [HoloViz](https://holoviz.org/) suite of tools