capitalise bib file

trchudley · Oct 22, 2024 · fc73032 · fc73032
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diff --git a/docs/appendix/faq.md b/docs/appendix/faq.md
diff --git a/docs/getting_started/why_pdemtools.md b/docs/getting_started/why_pdemtools.md
diff --git a/paper/paper.bib b/paper/paper.bib
@@ -1,13 +1,13 @@
 @misc{barnes_richdem_2016,
-    title = {RichDEM: Terrain Analysis Software}, 
+    title = {{RichDEM}: {Terrain} {Analysis} {Software}}, 
     author = {Barnes, Richard }, 
     year = {2016 - 2022}, 
     version = {2.3.1},
     url = {http://github.com/r-barnes/richdem}
 }
 
 @misc{corteva_rioxarray_2024,
-    author = {Corteva },
+    author = {Corteva},
     title = {rioxarray: geospatial xarray extension powered by rasterio},
     year = {2019 - 2024},
     version = {0.15.5},
@@ -27,15 +27,15 @@ @article{florinsky_computation_2009
 }
 
 @misc{foerste_geoid_2014,
-	title = {EIGEN-6C4 The latest combined global gravity field model including GOCE data up to degree and order 2190 of GFZ Potsdam and GRGS Toulouse},
+	title = {{EIGEN-6C4} {The} latest combined global gravity field model including {GOCE} data up to degree and order 2190 of {GFZ} {Potsdam} and {GRGS} {Toulouse}},
 	doi = {10.5880/icgem.2015.1},
 	publisher = {GFZ Data Services},
 	author = {Foerste, Christoph and Bruinsma, Sean. L. and Abrykosov, Oleh and Lemoine, Jean-Michel and Marty, Jean Charles and Flechtner, Frank and Balmino, Georges and Barthelmes, Franz and Biancale, Richard},
 	year = {2014},
 }
 
 @misc{glaciohack_xdem_2020,
-    title = {xDEM: Analysis of digital elevation models}, 
+    title = {{xDEM}: {Analysis} of digital elevation models}, 
     author = {{GlacioHack Community} }, 
     year = {2020 - 2023}, 
 	version = {0.0.18},

diff --git a/paper/paper.md b/paper/paper.md
@@ -40,7 +40,7 @@ bibliography: paper.bib
 
 [ArcticDEM](https://www.pgc.umn.edu/data/arcticdem/) and [REMA](https://www.pgc.umn.edu/data/rema/) are high-resolution, time-stamped 2-metre-resolution DEMs of the polar regions provided by the Polar Geospatial Center (PGC). They are extracted by applying stereo auto-correlation techniques [@noh_surface_2017] to pairs of submetre Maxar satellite imagery. The data includes Worldview-1, Worldview-2, Worldview-3, and GeoEye-1, beginning in 2007 (ArcticDEM) or 2009 (REMA) and ongoing to the present day. Products are available as tens of thousands of time-stamped 'strips' [@porter_arcticdem_2022; @howat_remastrips_2022] constructed from individual scene pairs, or as a single mosaic [@porter_arcticdem_2023; @howat_remamosaic_2022] compiled from the combined stack of strips. Strips allow users to perform change detection by comparing data from different seasons or years, whilst mosaics provide a consistent and comprehensive product over the entire polar regions.
 
-As Earth Science has moved into the 'big data' era, increasing amounts of Arctic- and Antarctic-focused resources are available as public, cloud-optimised datasets. New approaches are providing Python tools to act as combined API and processing tools, such as `icepyx` [@scheick_icepyx_2023] or `pypromice` [@how_pypromice_2023]. From 2022 (ArcticDEM v4.1 and REMA v2), the PGC DEM products are [hosted](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/pgc-data-stac.json) as Cloud Optimised GeoTIFFs (CoGs) in a SpatioTemporal Asset Catalog (STAC), a standardised structure for cataloguing spatiotemporal data. However, the PGC STAC is currently a 'static' rather than 'dynamic' STAC, which means there is no convenient Application Programming Interface (API) for searching the datasets in response to user queries. This limits the ability of users to programmatically interact with ArcticDEM and REMA data in a quick and efficient manner. The `pdemtools` package has two aims: the first is to provide a Python-focussed alternative for searching and downloading ArcticDEM and REMA data, emulating dynamic STAC query tools such as `pystac` [@radiant_pystac_2024]; whilst the second is to provide commonly used processing functions specific to the needs of ArcticDEM and REMA users (a focus on ice sheet and cryosphere work), as well as the particular strengths of the ArcticDEM and REMA datasets (high-resolution and multitemporal).
+As Earth Science has moved into the 'big data' era, increasing amounts of Arctic- and Antarctic-focused resources are available as public, cloud-optimised datasets. New approaches are providing Python tools to act as combined Application Programming Interface (API) and processing tools, such as `icepyx` [@scheick_icepyx_2023] or `pypromice` [@how_pypromice_2023]. From 2022 (ArcticDEM v4.1 and REMA v2), the PGC DEM products are [hosted](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/pgc-data-stac.json) as Cloud Optimised GeoTIFFs (CoGs) in a SpatioTemporal Asset Catalog (STAC), a standardised structure for cataloguing spatiotemporal data. However, the PGC STAC is currently a 'static' rather than 'dynamic' STAC, which means there is no convenient API for searching the datasets in response to user queries. This limits the ability of users to programmatically interact with ArcticDEM and REMA data in a quick and efficient manner. The `pdemtools` package has two aims: the first is to provide a Python-focussed alternative for searching and downloading ArcticDEM and REMA data, emulating dynamic STAC query tools such as `pystac` [@radiant_pystac_2024]; whilst the second is to provide commonly used processing functions specific to the needs of ArcticDEM and REMA users (a focus on ice sheet and cryosphere work), as well as the particular strengths of the ArcticDEM and REMA datasets (high-resolution and multitemporal).
 
 The `pdemtools` `search()` tool and `load` module allow for convenient access to the ArcticDEM and REMA datasets. Mosaics can be downloaded from a one-line `load.mosaic()` function, whilst the `search()` function allows for convenient filtering of a locally downloading ArcticDEM/REMA strip index according to variables such as date, region of interest, spatial coverage, temporal baseline, source sensors, accuracy, and cross-track data. The results of searches are returned as a `geopandas` dataframe [@jordahl_geopandas_2024], and can be downloaded using the `load.from_search()` function. Elevation models are returned as `xarray` DataArrays [@hoyer_xarray_2017] with geospatial metadata via the `rioxarray` extension [@corteva_rioxarray_2024] - a standard format for storing and processing n-dimensional geospatial data within the geospatial Python community. By utilising standardised formats, the aim is to allow the user to quickly move beyond `pdemtools` into their own analysis in whatever format they desire, be that `xarray`, `numpy` or `dask` datasets, DEM analysis Python packages such as `xdem` [@glaciohack_xdem_2020] for advanced coregistration or `richdem` [@barnes_richdem_2016] for flow analysis, or exporting to geospatial file formats for analysis beyond Python.