From 2ae1f66a88bd911297646245051d1465badc5478 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Tue, 9 May 2023 13:13:07 +0530 Subject: [PATCH 01/13] Create paper.md --- paper/paper.md | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 paper/paper.md diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..85d6481d --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,64 @@ +--- +title: 'pyOBIS: Python client for the OBIS API(https://api.obis.org/).' +tags: + - Python + - oceanography + - marine data +authors: + - name: Scott Chamberlain + equal-contrib: true + - name: Ayush Anand + equal-contrib: true + affiliation: 1 + - name: Tylar Murray + corresponding: true + affiliation: 2 + - name: Filipe Fernandes + corresponding: true +affiliations: + - name: National Institute of Technology Durgapur, India + index: 1 + - name: IMaRS University of South Florida, US + index: 2 +date: 9 May 2023 +bibliography: paper.bib + +# Optional fields if submitting to a AAS journal too, see this blog post: +# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing +aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. +aas-journal: Astrophysical Journal <- The name of the AAS journal. +--- + +# Summary + + +# Statement of need + +# Citations + +Citations to entries in paper.bib should be in +[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) +format. + +If you want to cite a software repository URL (e.g. something on GitHub without a preferred +citation) then you can do it with the example BibTeX entry below for @fidgit. + +For a quick reference, the following citation commands can be used: +- `@author:2001` -> "Author et al. (2001)" +- `[@author:2001]` -> "(Author et al., 2001)" +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" + +# Figures + +Figures can be included like this: +![Caption for example figure.\label{fig:example}](figure.png) +and referenced from text using \autoref{fig:example}. + +Figure sizes can be customized by adding an optional second parameter: +![Caption for example figure.](figure.png){ width=20% } + +# Acknowledgements + +We acknowledge contributions from Mathew Biddle during the genesis of this project. + +# References From 4fcb399192352222b68793efa9d27e7d33c8cd29 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Tue, 9 May 2023 13:14:17 +0530 Subject: [PATCH 02/13] remove AAS journal --- paper/paper.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 85d6481d..cbf02686 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -23,10 +23,6 @@ affiliations: date: 9 May 2023 bibliography: paper.bib -# Optional fields if submitting to a AAS journal too, see this blog post: -# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing -aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. -aas-journal: Astrophysical Journal <- The name of the AAS journal. --- # Summary From 24f5c82dcb49f2dd981d412e5fe3dea8ad0c35f1 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Tue, 9 May 2023 13:18:49 +0530 Subject: [PATCH 03/13] update author list --- paper/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paper/paper.md b/paper/paper.md index cbf02686..08879b0d 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -15,6 +15,8 @@ authors: affiliation: 2 - name: Filipe Fernandes corresponding: true + - name: Matthew Biddle + corresponding: true affiliations: - name: National Institute of Technology Durgapur, India index: 1 From af05bcc3429fc37fc6a83f28d5826d9f472d1a7c Mon Sep 17 00:00:00 2001 From: Mathew Biddle <8480023+MathewBiddle@users.noreply.github.com> Date: Fri, 2 Jun 2023 10:05:32 -0400 Subject: [PATCH 04/13] adjusting name spelling and adding affiliation --- paper/paper.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 08879b0d..fb5b58a0 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -15,13 +15,16 @@ authors: affiliation: 2 - name: Filipe Fernandes corresponding: true - - name: Matthew Biddle + - name: Mathew Biddle corresponding: true + affiliation: 3 affiliations: - name: National Institute of Technology Durgapur, India index: 1 - name: IMaRS University of South Florida, US index: 2 + - name: National Oceanic and Atmospheric Administration, National Ocean Service, Integrated Ocean Observing System, US + index: 3 date: 9 May 2023 bibliography: paper.bib From c7b0f1b31596a6422ac273b7269a7765e4f16909 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Sun, 4 Jun 2023 14:50:47 +0530 Subject: [PATCH 05/13] adds figures+summary+features --- paper/paper.md | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index fb5b58a0..0c7869ce 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -27,16 +27,20 @@ affiliations: index: 3 date: 9 May 2023 bibliography: paper.bib - --- # Summary - +The pyOBIS python package provides easy access to taxonomic occurrence records harvested from thousands of datasets. The package uses the API from the Ocean Biodiversity Information System (OBIS), a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, and sustainable development. Included in the pyOBIS package are built-in functions for accessing data on occurrences, taxon, nodes, checklists and datasets. The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. Coupled together with other libraries like pyDwcViz, it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. # Statement of need -# Citations +## Main Features +Here are just a few of things pyOBIS can do: +* Easy handling of OBIS data, easy fetching without handling the raw API response directly. +* Built-in functions for occurrence, taxon, node, checklist and dataset endpoints of OBIS API. +* Provides easy export of data to Pandas DataFrame, and helps researchers focus more on analysis rather than data mining. +# Citations Citations to entries in paper.bib should be in [rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) format. @@ -50,16 +54,14 @@ For a quick reference, the following citation commands can be used: - `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" # Figures +![Absolute Depth for Lepidochelys kempii over time.\label{fig:time-series-turtle}](https://github.com/ayushanand18/pyobis/assets/36472216/b6e66f31-7bbd-49c9-8186-3ab1a58e57c0) -Figures can be included like this: -![Caption for example figure.\label{fig:example}](figure.png) -and referenced from text using \autoref{fig:example}. - -Figure sizes can be customized by adding an optional second parameter: -![Caption for example figure.](figure.png){ width=20% } +pyOBIS can be used to do super-useful time series analysis for instance, absolute depth of Sea Turtle species, Lepidochelys kempii between 1990-2011 as shown in figure \autoref{fig:time-series-turtle}. From this analysis, the following observations can be made: +* The average depth has increased over the years, this means the species is looking for cooler waters to escape the heating waters. (This can be observed from the magenta-colored line which depicts the 5-year rolling average.) +* The species has witnessed a slight compression, i.e., minimal and maximal depth have come closer. For a brief period, it compressed significantly (around 2006) this might be due to data constraints or maybe some seasonal current. After that it has regained a lot but still the average difference in minimal and maximal depth is lower than early 2000s. +* However, necessary precautions to avoid sampling bias must be taken into consideration. # Acknowledgements - -We acknowledge contributions from Mathew Biddle during the genesis of this project. +We acknowledge the help of `Pandas`, `Matplotlib`, and `requests` python package, and all the authors for their contributions building this package, performing the associated analysis and drafting this manuscripts. # References From dfef213b586e2f0e7112ede4bde6afd4b09ff3ae Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Sun, 4 Jun 2023 14:56:43 +0530 Subject: [PATCH 06/13] changes title to more verbose --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 0c7869ce..bb865995 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'pyOBIS: Python client for the OBIS API(https://api.obis.org/).' +title: 'pyOBIS: easy access to taxonomic occurrence records harvested from thousands of datasets' tags: - Python - oceanography From 4eddb9b6f0367b6b2458ff8c28a371e6fd7ccc74 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Thu, 8 Jun 2023 16:15:59 +0530 Subject: [PATCH 07/13] update: introducing new section `Introduction` and breaking down `Summary` into multiple lines. --- paper/paper.md | 44 +++++++++++++++++++++++++++++--------------- 1 file changed, 29 insertions(+), 15 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index bb865995..10788898 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -30,9 +30,20 @@ bibliography: paper.bib --- # Summary -The pyOBIS python package provides easy access to taxonomic occurrence records harvested from thousands of datasets. The package uses the API from the Ocean Biodiversity Information System (OBIS), a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, and sustainable development. Included in the pyOBIS package are built-in functions for accessing data on occurrences, taxon, nodes, checklists and datasets. The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. Coupled together with other libraries like pyDwcViz, it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. +The pyOBIS python package provides easy access to taxonomic occurrence records harvested from thousands of datasets. +The package uses the API from the Ocean Biodiversity Information System (OBIS), +a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, +and sustainable development. +Included in the pyOBIS package are built-in functions for accessing data on occurrences, taxon, nodes, checklists and datasets. +The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, +and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. +Coupled together with other libraries like pyDwcViz, +it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. -# Statement of need +# Introduction + + +# Why pyOBIS? ## Main Features Here are just a few of things pyOBIS can do: @@ -40,19 +51,6 @@ Here are just a few of things pyOBIS can do: * Built-in functions for occurrence, taxon, node, checklist and dataset endpoints of OBIS API. * Provides easy export of data to Pandas DataFrame, and helps researchers focus more on analysis rather than data mining. -# Citations -Citations to entries in paper.bib should be in -[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) -format. - -If you want to cite a software repository URL (e.g. something on GitHub without a preferred -citation) then you can do it with the example BibTeX entry below for @fidgit. - -For a quick reference, the following citation commands can be used: -- `@author:2001` -> "Author et al. (2001)" -- `[@author:2001]` -> "(Author et al., 2001)" -- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" - # Figures ![Absolute Depth for Lepidochelys kempii over time.\label{fig:time-series-turtle}](https://github.com/ayushanand18/pyobis/assets/36472216/b6e66f31-7bbd-49c9-8186-3ab1a58e57c0) @@ -61,7 +59,23 @@ pyOBIS can be used to do super-useful time series analysis for instance, absolut * The species has witnessed a slight compression, i.e., minimal and maximal depth have come closer. For a brief period, it compressed significantly (around 2006) this might be due to data constraints or maybe some seasonal current. After that it has regained a lot but still the average difference in minimal and maximal depth is lower than early 2000s. * However, necessary precautions to avoid sampling bias must be taken into consideration. +# Conclusion + + # Acknowledgements We acknowledge the help of `Pandas`, `Matplotlib`, and `requests` python package, and all the authors for their contributions building this package, performing the associated analysis and drafting this manuscripts. # References + +# Citations +Citations to entries in paper.bib should be in +[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) +format. + +If you want to cite a software repository URL (e.g. something on GitHub without a preferred +citation) then you can do it with the example BibTeX entry below for @fidgit. + +For a quick reference, the following citation commands can be used: +- `@author:2001` -> "Author et al. (2001)" +- `[@author:2001]` -> "(Author et al., 2001)" +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" \ No newline at end of file From f34da53acf7d0fc21e930237d039e125ce41ea25 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Thu, 8 Jun 2023 16:25:41 +0530 Subject: [PATCH 08/13] update: reworded summary section to be less verbose and highlight more use cases. --- paper/paper.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 10788898..3745a66c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -34,13 +34,18 @@ The pyOBIS python package provides easy access to taxonomic occurrence records h The package uses the API from the Ocean Biodiversity Information System (OBIS), a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, and sustainable development. -Included in the pyOBIS package are built-in functions for accessing data on occurrences, taxon, nodes, checklists and datasets. -The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, -and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. +OBIS has more than 107 million occurrence records, making availibility of ocean data possible but accesibility remains a challenge. +pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadatas. +Users can download, visualize, segment, process and export data to any format of your choice with its built-in tools or rich ecosystem of libraries in python. Coupled together with other libraries like pyDwcViz, it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. # Introduction +OBIS is a global open-access data and information warehouse on marine biodiversity data. +It contains occurrence records, dataset metadatas, environmental data around species occurrences, +and many more biogeographic pointers. +The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, +and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. # Why pyOBIS? From a1c956e0e7f0fda7ab0fc12d66b9c1806941425a Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Thu, 8 Jun 2023 16:49:54 +0530 Subject: [PATCH 09/13] update: adds features and renames statement of need to `why pyobis` --- paper/paper.md | 40 +++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 3745a66c..625c2005 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,7 +37,7 @@ and sustainable development. OBIS has more than 107 million occurrence records, making availibility of ocean data possible but accesibility remains a challenge. pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadatas. Users can download, visualize, segment, process and export data to any format of your choice with its built-in tools or rich ecosystem of libraries in python. -Coupled together with other libraries like pyDwcViz, +Coupled together with other libraries like [pyDwcViz](https://github.com/marinebon/py-dwc-viz), it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. # Introduction @@ -49,12 +49,42 @@ and several included Jupyter notebooks demonstrate example analyses that can be # Why pyOBIS? +pyOBIS is intuitively split into different modules for querying IUCN red lists, +newly added species, datasets added, information on OBIS nodes, occurrence records, +MeasurementOrFacts, eDNA records, etc and searchable through unique IDs, taxa, scientific names, +geolocation, timestamps, and others. +The Taxa IDs used by OBIS is adopted from annotations by the WoRMS team thereby maintaining a uniform and universal identification convention. ## Main Features -Here are just a few of things pyOBIS can do: -* Easy handling of OBIS data, easy fetching without handling the raw API response directly. -* Built-in functions for occurrence, taxon, node, checklist and dataset endpoints of OBIS API. -* Provides easy export of data to Pandas DataFrame, and helps researchers focus more on analysis rather than data mining. +pyOBIS python package improvess accessibility of data available through OBIS +and helps reduce efforts in manipulating and visualizing Darwin Core Data. +Some of the key features of pyOBIS are: +* **Easy handling of OBIS data** + + Users can easily fetch data without handling the API directly. + The comprehensive documentation and built-in funtions provides support to both beginners and experienced researchers in handling Darwin Core Data. + Response is always returned as a custom object with pre-defined methods to export to a `pandas` DataFrame, + generate live API URL to plugin to any additional software, and + build an OBIS Mapper URL for direct one-click visualization on the OBIS Mapper portal. + +* **Smart download, processing and export of data** + + pyOBIS provides an interactive progress bar while fetching large occurrence records. + It also provides an estimated size of the request and the expected time to taken for the download. + pyOBIS un-nests entangled occurrence data, and increases readibility for beginner users. + It provides easy export of data to Pandas DataFrame, + so that researchers can export it to any format like `csv`, `excel`, `JSON` making data handling and compatibility + with other software super-easy. + +* **Richer support with sister packages** + + pyOBIS when coupled with sister packages e.g. `pyDwcViz` can be utilized to perform many important computations easily. + With one-line function and plug-and-play use, + users can generate biodiversity indices such as `ES50` and `Shannon's Index`, + get environment statistics from occurrence records queried for specified geo-spatial region of interest, + taxa, or other paramters, + generate interactive distribution plots with taxanomic heirarchy easily, + and many other possible use cases. # Figures ![Absolute Depth for Lepidochelys kempii over time.\label{fig:time-series-turtle}](https://github.com/ayushanand18/pyobis/assets/36472216/b6e66f31-7bbd-49c9-8186-3ab1a58e57c0) From c1511c3b063ab2be4b19cff9885e431b3704633d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Jun 2023 11:21:21 +0000 Subject: [PATCH 10/13] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- paper/paper.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 625c2005..9f378642 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,7 +35,7 @@ The package uses the API from the Ocean Biodiversity Information System (OBIS), a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, and sustainable development. OBIS has more than 107 million occurrence records, making availibility of ocean data possible but accesibility remains a challenge. -pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadatas. +pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadatas. Users can download, visualize, segment, process and export data to any format of your choice with its built-in tools or rich ecosystem of libraries in python. Coupled together with other libraries like [pyDwcViz](https://github.com/marinebon/py-dwc-viz), it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. @@ -61,7 +61,7 @@ and helps reduce efforts in manipulating and visualizing Darwin Core Data. Some of the key features of pyOBIS are: * **Easy handling of OBIS data** - Users can easily fetch data without handling the API directly. + Users can easily fetch data without handling the API directly. The comprehensive documentation and built-in funtions provides support to both beginners and experienced researchers in handling Darwin Core Data. Response is always returned as a custom object with pre-defined methods to export to a `pandas` DataFrame, generate live API URL to plugin to any additional software, and @@ -72,15 +72,15 @@ Some of the key features of pyOBIS are: pyOBIS provides an interactive progress bar while fetching large occurrence records. It also provides an estimated size of the request and the expected time to taken for the download. pyOBIS un-nests entangled occurrence data, and increases readibility for beginner users. - It provides easy export of data to Pandas DataFrame, - so that researchers can export it to any format like `csv`, `excel`, `JSON` making data handling and compatibility + It provides easy export of data to Pandas DataFrame, + so that researchers can export it to any format like `csv`, `excel`, `JSON` making data handling and compatibility with other software super-easy. * **Richer support with sister packages** - pyOBIS when coupled with sister packages e.g. `pyDwcViz` can be utilized to perform many important computations easily. - With one-line function and plug-and-play use, - users can generate biodiversity indices such as `ES50` and `Shannon's Index`, + pyOBIS when coupled with sister packages e.g. `pyDwcViz` can be utilized to perform many important computations easily. + With one-line function and plug-and-play use, + users can generate biodiversity indices such as `ES50` and `Shannon's Index`, get environment statistics from occurrence records queried for specified geo-spatial region of interest, taxa, or other paramters, generate interactive distribution plots with taxanomic heirarchy easily, @@ -113,4 +113,4 @@ citation) then you can do it with the example BibTeX entry below for @fidgit. For a quick reference, the following citation commands can be used: - `@author:2001` -> "Author et al. (2001)" - `[@author:2001]` -> "(Author et al., 2001)" -- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" \ No newline at end of file +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" From 107300793906ddb0a0403c7a995267130621be8d Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Thu, 22 Jun 2023 11:34:56 +0530 Subject: [PATCH 11/13] update(paper): adds conclusion to paper --- paper/paper.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 9f378642..39109cd2 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -95,7 +95,17 @@ pyOBIS can be used to do super-useful time series analysis for instance, absolut * However, necessary precautions to avoid sampling bias must be taken into consideration. # Conclusion - +The pyOBIS python package provides a convenient and efficient way to access and work with taxonomic occurrence records from the Ocean Biodiversity Information System (OBIS). +With over 107 million occurrence records, +OBIS is a valuable resource for marine biodiversity data, +but its accessibility has been a challenge which pyOBIS addresses by offering built-in functions for retrieving data on occurrences, taxons, nodes, checklists, and dataset metadata. +It enables researchers to download, +visualize, segment, process, and export data in various formats using its tools or other Python libraries. +By integrating with sister packages like pyDwcViz, +pyOBIS enhances its capabilities for analyzing Darwin Core Data with ease. +Overall, pyOBIS simplifies the handling of OBIS data, +facilitates data exploration and analysis, +and empowers researchers to study global and local species distributions across space and time. # Acknowledgements We acknowledge the help of `Pandas`, `Matplotlib`, and `requests` python package, and all the authors for their contributions building this package, performing the associated analysis and drafting this manuscripts. From f006582e36c32ee49af60501b4c7e667652203b9 Mon Sep 17 00:00:00 2001 From: Ayush Anand <36472216+ayushanand18@users.noreply.github.com> Date: Thu, 22 Jun 2023 11:35:28 +0530 Subject: [PATCH 12/13] update(paper): adds a Bib file for citations --- paper/paper.bib | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 paper/paper.bib diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..e69de29b From 1769f1cb208c38b6c8dc634dfb6c2bef5883412b Mon Sep 17 00:00:00 2001 From: Tylar Date: Tue, 1 Aug 2023 22:15:04 -0400 Subject: [PATCH 13/13] minor changes to first few paragraphs --- paper/paper.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 39109cd2..47723868 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -30,23 +30,22 @@ bibliography: paper.bib --- # Summary -The pyOBIS python package provides easy access to taxonomic occurrence records harvested from thousands of datasets. +The pyOBIS python package provides easy access to marine taxonomic occurrence records harvested from thousands of datasets. The package uses the API from the Ocean Biodiversity Information System (OBIS), a global open-access data and information clearinghouse on marine data for biodiversity for science, conservation, and sustainable development. -OBIS has more than 107 million occurrence records, making availibility of ocean data possible but accesibility remains a challenge. -pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadatas. +As of 2023, OBIS had more than 107 million occurrence records availibile, but accesibility remains a major challenge for oceanographic researchers. +pyOBIS solves the challenge by providing built-in functions for accessing data on occurrences, taxons, nodes, checklists, and dataset metadata. Users can download, visualize, segment, process and export data to any format of your choice with its built-in tools or rich ecosystem of libraries in python. Coupled together with other libraries like [pyDwcViz](https://github.com/marinebon/py-dwc-viz), -it forms an ecosystem of analysing Darwin Core Data with super ease through built-in functions. +it forms an ecosystem of tools for analyzing Darwin-Core-standardized data with super of ease through built-in functions. # Introduction OBIS is a global open-access data and information warehouse on marine biodiversity data. It contains occurrence records, dataset metadatas, environmental data around species occurrences, -and many more biogeographic pointers. -The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data mining, -and several included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. - +and other facts relevant for biogeographic research. +The package provides easy export of data to Pandas DataFrame to help researchers focus more on analysis rather than data munging. +Multiple included Jupyter notebooks demonstrate example analyses that can be used as a starting point for addressing research questions related to global and local distributions of species across space and time. # Why pyOBIS? pyOBIS is intuitively split into different modules for querying IUCN red lists, @@ -59,6 +58,7 @@ The Taxa IDs used by OBIS is adopted from annotations by the WoRMS team thereby pyOBIS python package improvess accessibility of data available through OBIS and helps reduce efforts in manipulating and visualizing Darwin Core Data. Some of the key features of pyOBIS are: + * **Easy handling of OBIS data** Users can easily fetch data without handling the API directly.