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1 -FENS-2022-poster-examples
1 +Practical Guide to Using the EBRAINS Knowledge Graph in (your) Research - User Examples
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4 4  (((
5 -= A Practical Guide to Using the EBRAINS Knowledge Graph in (your) Research =
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6 +**A Practical Guide to Using the EBRAINS Knowledge Graph in (your) Research**
6 6  
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7 7  User examples
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9 9  )))
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15 -(% class="wikigeneratedid" id="HAbstract" %)
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18 +[[image:Poster small.png]]
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16 16  **Abstract**
17 17  
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19 19  Scientific articles are mostly published as text files containing unstructured and semi-structured information. Consequently, important information is typically deeply hidden in documents which severely limits the possibilities to automatically process and reuse scholarly knowledge.  One approach to make information explicit and directly usable is to transform this into a standardised format and store it in a knowledge graph. This allows scholarly knowledge to be represented in a structured, machine-actionable, interlinked and semantically rich manner. The EBRAINS Knowledge Graph was developed to facilitate the search and information exchange in research, so that research results across different domains become directly comparable and easier to retrieve and reuse. Here, we provide a practical guide to extracting information from the EBRAINS Knowledge Graph using a user-friendly interface as well as a more advanced programmatic route via an Application Programming Interface (API). We also provide concrete examples on how the extracted information can be leveraged in order to develop new research objectives as well as validate ongoing research.  The EBRAINS Knowledge Graph is integrated in the wider EBRAINS research infrastructure as a part of the EBRAINS Data and Knowledge services for sharing and finding research data and models. Data found through these services can be directly used and analysed via the various integrated tools and analysis workflows. The EBRAINS Knowledge Graph is a valuable machine-actionable and FAIR (**F**indable, **A**ccessible, **I**nteroperable and **R**eusable) resource for discovery-based and hypothesis-driven research as it already contains a wide variety of neuroscience data types, models and software.
20 20  
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22 22  **What can I find here?**
23 23  
24 24  * Learn how to use the Knowledge Graph search user interface to extract information and download data
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25 25  * Learn how to build a simple query using the Knowledge Graph Query Builder
26 26  * Learn how to execute a query via python jupyter notebook
27 27  
28 -== Knowledge Graph Search UI ==
29 -
30 -The Knowledge Graph search user inferface is a user-friendly interface for accessing and extracting data and metadata. Use of the Knowledge Graph search is free and most datasets are open to the public. Human datasets with identifiable information can only be accessed with an EBRAINS account.
31 -
32 -Go to the Search UI: [[https:~~/~~/search.kg.ebrains.eu/>>https://search.kg.ebrains.eu/]]
33 -
34 -=== Refine search ===
35 -
36 -To find a dataset you can use the search filters:
37 -
38 -1. Select a category (i.e. Project, Dataset, Model, Software or Contributor
39 -1. Select a modality/experimental approach (e.g. electrophysiology)
40 -1. Select a species (e.g. mus musculus (mouse))
41 -
42 -[[image:SearchStep1-3.png||alt="Steps 1-3: Refine search" height="398" width="700"]]
43 -
44 -You can refine you search even further by using the free text search. Based on your search criteria, you get a number of hits. ([[https:~~/~~/search.kg.ebrains.eu/?facet_type[0]=Dataset&facet_Dataset_modalityForFilter[0]=electrophysiology&facet_Dataset_speciesFilter[0]=Mus%20musculus>>https://search.kg.ebrains.eu/?facet_type[0]=Dataset&facet_Dataset_modalityForFilter[0]=electrophysiology&facet_Dataset_speciesFilter[0]=Mus%20musculus]])
45 -
46 -=== Select a dataset ===
47 -
48 -When you select a dataset, you can read find a summary of the metadata on the dataset card (see example below).
49 -
50 -[[image:SearchStep4.png||alt="Step 4: Dataset Card" height="500" width="700"]]
51 -
52 -=== Access detailed metadata ===
53 -
54 -In the side panel, you can find an overview of the metadata that is available for this dataset ([[https:~~/~~/search.kg.ebrains.eu/instances/7866daf2-7064-4fa0-b6a2-0b1c899ba35f>>https://search.kg.ebrains.eu/instances/7866daf2-7064-4fa0-b6a2-0b1c899ba35f]]). For example:
55 -
56 -1. The data descriptor is a pdf text file with detailed information about the dataset, including the methods, technical validation, usage notes and data organisation.
57 -1. Detailed subject metadata can be found in a separate tab (see below).
58 -
59 -[[image:SearchStep5.png||alt="Step 5: Subject metadata" height="400" width="700"]]
60 -
61 -In this dataset, a number of subjects were used with a C57BL/6J (wildtype) genotype. These were 4-9 months old (adult mice) with a weight between 38 - 47 gram. They were tested three times under aneasthesia, which is captured by the "studied states" of the subject. Additional details regarding the tests is stored under additional remarks (currently not visible in the image).
62 -
63 -== Programmatic access (API) ==
64 -
65 -The EBRAINS Knowledge Graph provides convenient tools and APIs for the implementation of queries into your scripts.
66 -
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68 -**Request permission**
69 -
70 -To be able to access the Knowledge Graph (KG) programmatically, you require:
71 -
72 -1. An EBRAINS account. Register for an account here: [[https:~~/~~/ebrains.eu/register/>>https://ebrains.eu/register/]]
73 -1. Register and request credentials for your KG client by emailing support@ebrains.eu
74 -
75 -=== Query Builder ===
76 -
77 -The query builder allows you to make, save, and reuse complex queries without any knowledge of graph query languages.
78 -
79 -Go to the Query Builder: [[https:~~/~~/query.kg.ebrains.eu>>https://query.kg.ebrains.eu/#state=6a41dd5b-3cf9-4263-8a65-83d41f27677a&session_state=638c33f5-4551-4d70-8b1d-a5c900aa62d0&code=300f25d8-caac-4576-9a21-195ccc93fd31.638c33f5-4551-4d70-8b1d-a5c900aa62d0.4214dba9-b6d0-4331-9837-3e75a10cda14]]
80 -
81 -
82 -==== Select a category ====
83 -
84 -A query for the Knowledge Graph is always executed against a specific data structure (e.g. a "Dataset version"). First select the data structure for your query.
85 -
86 -[[image:QB1.png||alt="Step 1: Select a category" height="520" width="700"]]
87 -
88 -
89 -==== Select an experimental approach ====
90 -
91 -Similar to the facetted filters in the Knowledge Graph Search UI, we can refine our query by defining the experimental approach of our interest, for example electrophysiology.
92 -
93 -To ensure that all dataset versions with electrophysiology are queried, and none of the dataset versions without electrophysiology, we make this property required and we apply a filter to the label we are searching for (i.e. electrophysiology).
94 -
95 -[[image:QBstep2.png||alt="Step 2: Select modality" height="541" width="700"]]
96 -
97 -
98 -==== Select species ====
99 -
100 -Similarly, we can refine our query for mice only.
101 -
102 -[[image:QBstep3.png||alt="Step 3: Select species" height="543" width="700"]]
103 -
104 -
105 -==== Access detailed metadata ====
106 -
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113 113  {{box title="**Contents**"}}
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114 114  {{toc/}}
115 115  {{/box}}
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