updated paper url

README.md

@@ -12,7 +12,7 @@
 
 # Open-ST: open-source spatial transcriptomics
 
-### [🌐 website](https://rajewsky-lab.github.io/openst/latest) | [📜 paper](https://authors.elsevier.com/c/1jJckL7PXqR3U) | [🐁 datasets](https://rajewsky-lab.github.io/openst/latest/examples/datasets/)
+### [🌐 website](https://rajewsky-lab.github.io/openst/latest) | [📜 paper](https://doi.org/10.1016/j.cell.2024.05.055) | [🐁 datasets](https://rajewsky-lab.github.io/openst/latest/examples/datasets/)
 
 Open-ST is an open-source spatial transcriptomics method 
 with efficient whole-transcriptome capture at sub-cellular resolution (0.6 μm) at low cost 

docs/examples/getting_started.md

@@ -6,7 +6,7 @@ leveraging the **Open-ST** workflow (experimental and computational).
 ## Datasets
 
 We provide raw data, instructions on how to reproduce the preprocessing pipeline, the corresponding preprocessed
-data (for comparison), and notebooks for some exploratory visualization and downstream analysis. We publicly provide mouse datasets, showcased in the [paper](https://authors.elsevier.com/c/1jJckL7PXqR3U).
+data (for comparison), and notebooks for some exploratory visualization and downstream analysis. We publicly provide mouse datasets, showcased in the [paper](https://doi.org/10.1016/j.cell.2024.05.055).
 
 <div class="grid cards" markdown>
 

docs/experimental/capture_area_generation.md

@@ -10,7 +10,7 @@ For instance, you can get ~360 capture areas sized 3x4 mm from a single **Illumi
 
 When using an **Illumina® NovaSeq 6000 S4** flow cell (35 cycles), sequence the HDMI32-DraI library
 (see in [Oligonucleotides](getting_started.md)) at a loading concentration of 200 pM. 
-Using 200 pM library, loaded according to the KAPA qPCR value, we obtained the following quality metrics for the barcoded fc_1 used in our [our paper](https://authors.elsevier.com/c/1jJckL7PXqR3U): Q30 >= 86%; PF = 78%; occupied = 97%. Although great results were achieved using this flow cell, 97% occupied is high. Consequently, we suggest to use a titration of library loading concentrations (one concentration per lane) when generating your first barcoded flow cell. 
+Using 200 pM library, loaded according to the KAPA qPCR value, we obtained the following quality metrics for the barcoded fc_1 used in our [our paper](https://doi.org/10.1016/j.cell.2024.05.055): Q30 >= 86%; PF = 78%; occupied = 97%. Although great results were achieved using this flow cell, 97% occupied is high. Consequently, we suggest to use a titration of library loading concentrations (one concentration per lane) when generating your first barcoded flow cell. 
 
 Sequence a single-end 37 cycle read, using Read1-DraI oligo as a custom primer.
 Use a custom sequencing [recipe](../static/metadata_files/NovaSeq6000_S4_barcoding_seq_recipe.xml) that stops the run immediately after read 1 prior to on-instrument washes. 

docs/introduction.md

@@ -1,6 +1,6 @@
 Welcome! Here, we provide comprehensive resources to help you generate and analyse Open-ST data in your lab.
 
-Open-ST is a spatial transcriptomics method that enables efficient whole-transcriptome capture at subcellular resolution. In [our paper](https://authors.elsevier.com/c/1jJckL7PXqR3U), we have demonstrated Open-ST's wide applicability, showing robust transcriptome capture across various mouse and human tissues. 
+Open-ST is a spatial transcriptomics method that enables efficient whole-transcriptome capture at subcellular resolution. In [our paper](https://doi.org/10.1016/j.cell.2024.05.055), we have demonstrated Open-ST's wide applicability, showing robust transcriptome capture across various mouse and human tissues. 
 Our method is cost-efficient, straightforward to employ, and includes open-source software for seamless data processing and analysis.
 
 Here, you can find detailed step-by-step descriptions of the experimental and the computational workflows. In a FAQ section we address commonly asked questions. Via our [discussion board] and our [chat], you can submit your own questions, and we will do our best to provide answers. 

docs/theme_override_home/home.html

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           <a href="{{ page.next_page.url | url }}" title="{{ page.next_page.title | striptags }}" class="md-button md-button--primary">
             Get started
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-          <a href="https://authors.elsevier.com/c/1jJckL7PXqR3U" title="Paper" class="md-button">
+          <a href="https://doi.org/10.1016/j.cell.2024.05.055" title="Paper" class="md-button">
             Paper
           </a><br>
           <a href="{{ config.repo_url }}" title="{{ lang.t('source.link.title') }}" class="md-button">