├── .github └── issue_template │ ├── README.md │ └── data-source-template.md ├── README.md ├── SUMMARY.md ├── adding-data.md ├── diagrams ├── CovidGraph_Skills.graphml ├── CovidGraph_Skills_white.graphml ├── CovidGraph_Systems.graphml ├── Documentation - Docusaurus.graphml ├── Documentation - Structr.graphml ├── Documentation and Issues.graphml ├── Inbound Channels.graphml ├── Pipeline.graphml ├── Schema.graphml ├── Schema_NLP.graphml ├── covidgraph_modules.drawio └── exports │ ├── CovidGraph_Skills_white_transparent.png │ ├── CovidGraph_Skillsv2.png │ ├── CovidGraph_Skillsv2_transparent.png │ ├── CovidGraph_Skillsv3_transparent.png │ ├── CovidGraph_Skillsv4_transparent.png │ ├── CovidGraph_Systemsv1.0.png │ ├── Documentation - Docusaurusv1.0.png │ ├── Documentation - Structrv1.0.png │ ├── Documentation and Issues.png │ ├── Inbound Channels.png │ ├── Pipelinev0.1.png │ ├── Schema v0.1.png │ ├── Schema v0.2.png │ ├── Schema v0.3.png │ ├── Schema v0.4.png │ ├── Schema v0.5.png │ ├── Schema v1.0.png │ ├── Schema v1.0_grey_bg.png │ ├── Schema v1.1.png │ ├── Schema v1.1_grey_bg.png │ ├── Schema v1.2.png │ ├── Schema_v1.0_no_logo.png │ ├── Schema_v1.1_no_logo.png │ └── covidgraph_modules.png ├── helpful-queries.md ├── label-details.md ├── legal-statement.md ├── nodes_and_relationships.md ├── privacy-policy.md ├── screenshots ├── accessing-data │ ├── connect_neo4j_browser.png │ ├── database_information.png │ ├── documents_neo4j.png │ └── saved_scripts.png └── zenhub │ ├── cord-19-epic-detail.png │ ├── cord-19-epic-filter-highlights.png │ ├── cord-19-epic-filter-highlights.svg │ ├── data-source-label-filter-highlight.png │ ├── managing-issues.png │ ├── new-issue-choose-repo.png │ ├── new-issue-choose-template.png │ ├── vge-repo-filter-highlight.png │ ├── zenhub-default-view.png │ ├── zenhub-tab-highlight.png │ └── zenhub-tab.svg ├── untitled.md └── website ├── README.md └── content ├── English Edit - PR-03-20-Neo4j-COVID-19 ├── README.md └── datasources.md /.github/issue_template/README.md: -------------------------------------------------------------------------------- 1 | # ISSUE\_TEMPLATE 2 | 3 | -------------------------------------------------------------------------------- /.github/issue_template/data-source-template.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Data Source Template 3 | about: Use this template to log new data sources. 4 | title: Data Source Name 5 | labels: 'data source, suggested' 6 | assignees: '' 7 | --- 8 | 9 | # data-source-template 10 | 11 | Please select 'Create an Epic' and NOT issue below. 12 | 13 | ## Data Source 14 | 15 | \(url to data source\) 16 | 17 | ## Note 18 | 19 | Add a description of the data source 20 | 21 | ## Script to load 22 | 23 | \(url to script to load data\) 24 | 25 | ## Idempotent 26 | 27 | Yes or No 28 | 29 | ## Dependencies 30 | 31 | None or link to related data sources 32 | 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CovidGraph Wiki 2 | 3 | [Main Wiki Page](https://github.com/covidgraph/documentation/wiki) 4 | 5 | -------------------------------------------------------------------------------- /SUMMARY.md: -------------------------------------------------------------------------------- 1 | # Table of contents 2 | 3 | * [CovidGraph Wiki](README.md) 4 | * [website](website/README.md) 5 | * [content](website/content/README.md) 6 | * [datasources](website/content/datasources.md) 7 | * [Legal Statement](legal-statement.md) 8 | * [.github](.github/README.md) 9 | * [ISSUE\_TEMPLATE](.github/issue_template/README.md) 10 | * [data-source-template](.github/issue_template/data-source-template.md) 11 | * [privacy-policy](privacy-policy.md) 12 | * [adding-data](adding-data.md) 13 | * [helpfull-queries](helpfull-queries.md) 14 | * [Untitled](untitled.md) 15 | 16 | -------------------------------------------------------------------------------- /adding-data.md: -------------------------------------------------------------------------------- 1 | # adding-data 2 | 3 | Content moved to [https://github.com/covidgraph/documentation/wiki/Adding-your-own-data-to-the-graph](https://github.com/covidgraph/documentation/wiki/Adding-your-own-data-to-the-graph) 4 | 5 | -------------------------------------------------------------------------------- /diagrams/Inbound Channels.graphml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | CovidGraph.org 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | CovidGraph.org 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | GET IN TOUCH 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | Folder 2 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | SUBMIT FORM 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | EMAIL 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | MESSAGE 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | NAME 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | APPLICATIONS 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | Folder 2 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | FEEDBACK FORM? 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | PUBLISH VGE EMAIL 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | VISUAL GRAPH EXPLORER 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | MAIL SERVER 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | CovidGraph.org 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | CONTACT@COVIDGRAPH.ORG 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | VGE@COVIDGRAPH.ORG 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | EMAILS ON CC / REDIRECT 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | DIRECT ENQUIRIES FROM THIRD PARTIES 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | DIRECT FEEDBACK FROM THIRD PARTIES 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | GITHUB 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | CovidGraph.org 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | VGE REPO 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | CovidGraph.org 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | NEW ISSUE 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | VGE@FIRE.FUNDERSCLUB.COM 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | CRM SYSTEM 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | CovidGraph.org 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | NOTE - Alexander & I will be 486 | looking at FundraisingBox 487 | as an alternative CRM system 488 | 489 | For now Contact forms/email only go 490 | to contact@covidgraph.org + any 491 | notification cc's 492 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 | 515 | 516 | 517 | 518 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 | 529 | 530 | 531 | 532 | 533 | 534 | 535 | 536 | 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 578 | 579 | 580 | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | 590 | 591 | SERVER-SIDE REDIRECT 592 | 593 | 594 | 595 | 596 | 597 | 598 | 599 | 600 | 601 | -------------------------------------------------------------------------------- /diagrams/Pipeline.graphml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | TEXT_GENE_MATCH 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | CREATE_FULL_TEXT_INDEXES 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | JHU 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | BIOBASE 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | LENS-PATENT-DATA 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | CORD-19 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | TEXT_FRAGGER 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | GitHub/CovidGraph/ 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | GitHub/CovidGraph/ 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | graph-processing_text_gene_match 139 | Matches GeneSymbol in Fragment nodes and creates an indes with custom analyser on Fragment nodes 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | graph-processing_fragmentize_text 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | Creates fragment nodes for publications 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | data-cord19 181 | Script to transform data set from the Covid-19 Open Research Dataset (CORD)Challenge into a Neo4j graph 182 | Data Sources: 183 | - CORD-19 Research Challenge 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | data-lens-org-covid19-patents 196 | Transforms the data set from the Lens.org Covid19 patent data set into a Neo4j graph 197 | Data Sources: 198 | - Lens.org 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | data_biobase 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | data_jhu_population 224 | Case statistics from JHU and UN World Population data 225 | Data Sources: 226 | - JHU Statistics 227 | - UN Statistics 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | graph-processing-fulltext-indexes 240 | Script to create the fulltext indexes needed in CovidGraph 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | data_hetionet 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | data-clinical_trials_gov 265 | Transforms data from ClinicalTrials.gov into a Neo4j graph 266 | Data Sources: 267 | - ClinicalTrials.gov API 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | data-biobert 280 | Adds subset of preprocessed data of https://bern.korea.ac.kr/ into the Covidgraph 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | HELOMICS_HETIONET 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | CLINICAL_TRIALS_GOV 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | BIOBERT 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 492 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 | 515 | 516 | 517 | 518 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 | 529 | 530 | 531 | 532 | -------------------------------------------------------------------------------- /diagrams/covidgraph_modules.drawio: -------------------------------------------------------------------------------- 1 | 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/diagrams/exports/Schema_v1.1_no_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/covidgraph/documentation/7a3c7f03caadac8b7b732fd13162bbeb38485358/diagrams/exports/Schema_v1.1_no_logo.png -------------------------------------------------------------------------------- /diagrams/exports/covidgraph_modules.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/covidgraph/documentation/7a3c7f03caadac8b7b732fd13162bbeb38485358/diagrams/exports/covidgraph_modules.png -------------------------------------------------------------------------------- /helpful-queries.md: -------------------------------------------------------------------------------- 1 | # helpful-queries 2 | 3 | [Back to CovidGraph Wiki](https://github.com/covidgraph/documentation/wiki) 4 | 5 | ## Helpful queries on the CovidGraph 6 | 7 | 8 | Queries marked with a ⛔ need a review by its maintainer 9 | 10 | ### Data model 11 | 12 | ```cypher 13 | call db.schema.visualization() yield nodes, relationships 14 | return 15 | [x in nodes where apoc.any.properties(x).name in ['Abstract', 'AbstractCollection', 'Affiliation', 'AgeGroup', 'Author', 'AuthorCollection', 'BodyText', 'BodyTextCollection', 'Citation', 'City', 'ClinicalTrial', 'Country', 'DailyReport', 'Entity', 'ExclusionCriteria', 'Facility', 'GOTerm', 'Gene', 'GeneSymbol', 'GtexDetailedTissue', 'GtexTissue', 'InclusionCriteria', 'Location', 'Paper', 'Patent', 'PatentAbstract', 'PatentClaim', 'PatentDescription', 'PatentLiteratureCitation', 'PatentNumber', 'PatentTitle', 'Pathway', 'Phase', 'Protein', 'Province', 'Reference', 'ReferenceCollection', 'Transcript','Fragment']], 16 | [x in relationships where not type(x) in ['INVENTOR']] 17 | ``` 18 | 19 | ### Papers 20 | 21 | * Fulltext search in papers 22 | CALL db.index.fulltext.queryNodes("textOfPapersAndPatents", $1) YIELD node 23 | match (node)<-[:HAS_FRAGMENT]-(ab:Abstract)<-[:ABSTRACTCOLLECTION_HAS_ABSTRACT]-(abc:AbstractCollection)<-[:PAPER_HAS_ABSTRACTCOLLECTION]-(paper:Paper) 24 | RETURN paper 25 | 26 | * List Fulltext papers with title 27 | 28 | ```cypher 29 | MATCH (p:Paper)-[pr:PAPER_HAS_BODYTEXTCOLLECTION]->(c:BodyTextCollection)-[r:BODYTEXTCOLLECTION_HAS_BODYTEXT]->(t:BodyText) 30 | WITH p.title as title,collect({txt:t.text, pos:r.position}) as text 31 | UNWIND text as t 32 | WITH title, t 33 | order by t.pos 34 | RETURN title, collect(t.txt) 35 | limit 4 36 | ``` 37 | 38 | * Get Papers and Authors 39 | 40 | ```cypher 41 | MATCH (a:Author)<-[:AUTHORCOLLECTION_HAS_AUTHOR]-(:AuthorCollection)<-[:PAPER_HAS_AUTHORCOLLECTION]-(p:Paper) 42 | RETURN a, p, apoc.create.vRelationship(a, 'AUTHORED',{}, p) as vrel 43 | limit 100 44 | ``` 45 | 46 | * Genes connected to papers 47 | 48 | 49 | ```cypher 50 | MATCH (p:Paper) 51 | MATCH (p)-[:PAPER_HAS_BODYTEXTCOLLECTION]->(:BodyTextCollection)-[:BODYTEXTCOLLECTION_HAS_BODYTEXT]->(:BodyText)-[:HAS_FRAGMENT]->(f:Fragment)-[:MENTIONS]->(g:GeneSymbol) 52 | RETURN p,g, apoc.create.vRelationship(p,"MENTIONS",{},g) as rel 53 | limit 50 54 | ``` 55 | 56 | * Number of authors by location/region 57 | 58 | 59 | ```cypher 60 | MATCH (loc:Location)<-[:AFFILIATION_HAS_LOCATION]-(aff:Affiliation)-[:AUTHOR_HAS_AFFILIATION]-(a:Author) 61 | WHERE loc.country IS NOT NULL 62 | RETURN loc.country as country, loc.region as region, count(distinct a.email) AS NbrOfAuthors 63 | ORDER BY count(distinct a.email) DESC 64 | ``` 65 | 66 | * Titles of papers with a specific keyword \(e.g. Virus\), ordered by date of publication. 67 | 68 | ```cypher 69 | MATCH (p:Paper) 70 | WHERE p.title IS NOT NULL AND p.title CONTAINS("Virus") 71 | RETURN p.title, p.publish_time 72 | ORDER BY p.publish_time DESC 73 | LIMIT 20 74 | ``` 75 | 76 | * Number of papers whose Title text contains a user-specified keyword \(e.g. Virus\). 77 | 78 | ```cypher 79 | MATCH (p:Paper) 80 | WHERE p.title IS NOT NULL AND p.title CONTAINS("Virus") 81 | RETURN count(p) 82 | LIMIT 20 83 | ``` 84 | 85 | ### Patents 86 | 87 | * Find genes and proteins that are mentioned in patents 88 | 89 | 90 | ⛔ Not working atm 91 | 92 | ```cypher 93 | match path=(e:Entity)<-[x:APPLICANT]-(p:Patent)-[y:PATENT_HAS_PATENTCLAIM|:PATENT_HAS_PATENTABSTRACT|:PATENT_HAS_PATENTTITLE]->(pa)-[z:HAS_FRAGMENT]->(ff:Fragment)-[m:MENTIONS]->(syn:GeneSymbol)-[:SYNONYM]->(gs:GeneSymbol)<-[:MAPS]-(g:Gene)-[:CODES]->(tc:Transcript)-[:CODES]->(pro:Protein) 94 | where e.idLower starts with $company and exists(pro.name) 95 | return path limit 100 96 | ``` 97 | 98 | * Does company xyz work on protein xxx? 99 | 100 | 101 | ⛔ Not working atm 102 | 103 | ```cypher 104 | match path=(e:Entity)<-[x:APPLICANT]-(p:Patent)-[y:PATENT_HAS_PATENTCLAIM|:PATENT_HAS_PATENTABSTRACT|:PATENT_HAS_PATENTTITLE]->(pa)-[z:HAS_FRAGMENT]->(ff:Fragment)-[m:MENTIONS]->(syn:GeneSymbol)-[:SYNONYM]->(gs:GeneSymbol)<-[:MAPS]-(g:Gene)-[:CODES]->(tc:Transcript)-[:CODES]->(pro:Protein) 105 | where e.idLower starts with $company and pro.name contains $protein 106 | return path limit 40 107 | ``` 108 | 109 | * Find gene names mentioned in patents 110 | 111 | 112 | ```cypher 113 | match (p:Patent)-[x:PATENT_HAS_PATENTCLAIM|:PATENT_HAS_PATENTABSTRACT|:PATENT_HAS_PATENTTITLE]-(pct)-[:HAS_FRAGMENT]->(f2:Fragment)-[:MENTIONS]->(gs2:GeneSymbol) return p,x,pct,gs2 limit 300 114 | ``` 115 | 116 | * Search patents with string against a textindex and get a hit score 117 | 118 | ```cypher 119 | call db.index.fulltext.queryNodes("PatentsFulltextIndex","Corona") 120 | yield node,score match (node)--(p:Patent)--(pt:PatentTitle) 121 | return distinct(p.id) as id, collect(pt.text) as titles, labels(node)[0] as found_type, node.lang as found_in_lang ,score 122 | order by score 123 | desc limit 10 124 | ``` 125 | 126 | ⛔ Freezes atm 127 | 128 | ```cypher 129 | call db.index.fulltext.queryNodes("fragmentGeneSymbol","corona and virus") 130 | yield node as f,score match (f)--(px)--(p:Patent) 131 | match (fp:Fragment)-[:NEXT]->(f),(f)-[:NEXT]->(fn:Fragment) 132 | return f.kind,[fp.text,f.text,fn.text],p.id,score 133 | order by score desc 134 | limit 10 135 | ``` 136 | 137 | * Find matching fragments in patent text 138 | 139 | ⛔ Freezes atm 140 | 141 | ```cypher 142 | call db.index.fulltext.queryNodes("fragmentGeneSymbol","corona and virus") 143 | yield node as f,score match (f)--(px)--(p:Patent) 144 | return f.kind,f.text,p.id,score 145 | order by score desc 146 | limit 10 147 | ``` 148 | 149 | * This shows the previous and next fragment in the result 150 | 151 | 152 | ⛔ Freezes atm 153 | 154 | ```cypher 155 | call db.index.fulltext.queryNodes("fragmentGeneSymbol","corona and virus") 156 | yield node as f,score match (f)--(px)--(p:Patent) 157 | match (fp:Fragment)-[:NEXT]->(f),(f)-[:NEXT]->(fn:Fragment) 158 | return f.kind,fp.text,f.text,fn.text,p.id,score 159 | order by score desc 160 | limit 10 161 | ``` 162 | 163 | ### Authors 164 | 165 | * Ranking authors. First create a projection on the graph, then call the PageRank algorithm: 166 | 167 | ```cypher 168 | CALL gds.graph.create.cypher( 169 | 'Authors_Influence', 170 | 'MATCH (n:Author) RETURN id(n) AS id', 171 | 'MATCH (a:Author)-[:AUTHOR_HAS_AUTHOR]->(b:Author) RETURN id(a) AS source, id(b) AS target' 172 | ) 173 | YIELD graphName, nodeCount, relationshipCount, createMillis; 174 | ``` 175 | 176 | ```cypher 177 | CALL gds.pageRank.stream('Authors_Influence') YIELD nodeId, score RETURN gds.util.asNode(nodeId).first, gds.util.asNode(nodeId).last, score ORDER BY score DESC 178 | ``` 179 | 180 | ```cypher 181 | CALL db.index.fulltext.queryNodes("AuthorFullTextIndex", $word) YIELD node 182 | RETURN node 183 | ``` 184 | 185 | ### Bloom queries 186 | 187 | * Look for patents 188 | 189 | ```cypher 190 | CALL db.index.fulltext.queryNodes("textOfPapersAndPatents", $1) YIELD node 191 | with node 192 | where node:Patent 193 | return node 194 | ``` 195 | 196 | * Look for papers 197 | 198 | ```cypher 199 | CALL db.index.fulltext.queryNodes("textOfPapersAndPatents", $1) YIELD node 200 | match (node)<-[:HAS_FRAGMENT]-(ab:Abstract)<-[:ABSTRACTCOLLECTION_HAS_ABSTRACT]-(abc:AbstractCollection)<-[:PAPER_HAS_ABSTRACTCOLLECTION]-(paper:Paper) 201 | RETURN paper 202 | ``` 203 | 204 | * Look for authors 205 | 206 | ```cypher 207 | CALL db.index.fulltext.queryNodes("AuthorFullTextIndex", $word) YIELD node 208 | RETURN node 209 | ``` 210 | 211 | * Text containing keywords x and y 212 | 213 | ```cypher 214 | CALL db.index.fulltext.queryNodes("textOfPapersAndPatents", '$1 AND $2') YIELD node 215 | match (node)<-[:HAS_FRAGMENT]-()<-[:ABSTRACTCOLLECTION_HAS_ABSTRACT|PAPER_HAS_ABSTRACTCOLLECTION|PATENT_HAS_PATENTTITLE|PATENT_HAS_PATENTCLAIM|PATENT_HAS_PATENTABSTRACT*1..2]-(pp) where node:Fragment and not node:AbstractCollection 216 | and not node:BodyText 217 | RETURN pp limit 50 218 | ``` 219 | 220 | ### NLP BERN Entities 221 | 222 | * List entity types with count and if they are linked (external ids) 223 | 224 | ```cypher 225 | MATCH (n:NamedEntity) 226 | RETURN n.type as type, exists(n.external_ids) as external_ids, count(*) 227 | ``` 228 | -------------------------------------------------------------------------------- /label-details.md: -------------------------------------------------------------------------------- 1 | 2 | Priority: High 3 | Label name 4 | Priority: High 5 | Description 6 | This issue should be dealt with as soon as possible 7 | Color 8 | 212529 9 | 10 | Priority: Low 11 | Label name 12 | Priority: Low 13 | Description 14 | This issue can be dealt with in long term or is on hold 15 | Color 16 | e9ecef 17 | 18 | Priority: Normal 19 | Label name 20 | Priority: Normal 21 | Description 22 | This issue can be dealt with when possible 23 | Color 24 | adb5bd 25 | 26 | Status: Clarification Needed 27 | Label name 28 | Status: Clarification Needed 29 | Description 30 | Additional information is needed to deal with this issue 31 | Color 32 | c77dff 33 | 34 | Status: In Dev 35 | Label name 36 | Status: In Dev 37 | Description 38 | This issue has been moved to the Dev environment for testing 39 | Color 40 | 7b2cbf 41 | 42 | Status: In Prod 43 | Label name 44 | Status: In Prod 45 | Description 46 | This issue is live in the Production environment 47 | Color 48 | 240046 49 | 50 | Status: Suggested 51 | Label name 52 | Status: Suggested 53 | Description 54 | This issue is a suggestion for doing something new or different in CovidGraph 55 | Color 56 | e0aaff 57 | 58 | Tag: Documentation 59 | Label name 60 | Tag: Documentation 61 | Description 62 | About CovidGraph Documentation 63 | Color 64 | ffffff 65 | 66 | Tag: Duplicate 67 | Label name 68 | Tag: Duplicate 69 | Description 70 | This issue or pull request already exists 71 | Color 72 | ffffff 73 | 74 | Tag: Good First Issue 75 | Label name 76 | Tag: Good First Issue 77 | Description 78 | Good for newcomers 79 | Color 80 | ffffff 81 | 82 | Tag: Help Wanted 83 | Label name 84 | Tag: Help Wanted 85 | Description 86 | Extra attention is needed 87 | Color 88 | ffffff 89 | 90 | Tag: Infrastructure 91 | Label name 92 | Tag: Infrastructure 93 | Description 94 | This issue is about infrastructure 95 | Color 96 | ffffff 97 | 98 | Tag: Numeric 99 | Label name 100 | Tag: Numeric 101 | Description 102 | This data source contains numeric or statistical data 103 | Color 104 | ffffff 105 | 106 | Tag: Patient Data 107 | Label name 108 | Tag: Patient Data 109 | Description 110 | This data source contains patient data 111 | Color 112 | ffffff 113 | 114 | Type: Bug 115 | Label name 116 | Typ: Bug 117 | Description 118 | Something isn't working 119 | Color 120 | 1b4332 121 | 122 | Type: Data Analysis 123 | Label name 124 | Type: Data Analysis 125 | Description 126 | To identify an issue as data analysis 127 | Color 128 | 2d6a4f 129 | 130 | Type: Data Source 131 | Label name 132 | Type: Data Source 133 | Description 134 | To identify an issue as a data source 135 | Color 136 | 1b4332 137 | 138 | Type: Feature 139 | Label name 140 | Type: Feature 141 | Description 142 | To identify an issue as a feature 143 | Color 144 | 52b788 145 | 146 | Type: Question 147 | Label name 148 | Type: Question 149 | Description 150 | This issue raises a question for discussion 151 | Color 152 | d8f3dc 153 | 154 | Type: Use Case 155 | Label name 156 | Type: Use Case 157 | Description 158 | To identify an issue as a use case 159 | Color 160 | 95d5b2 161 | -------------------------------------------------------------------------------- /legal-statement.md: -------------------------------------------------------------------------------- 1 | # Legal Statement 2 | 3 | ## Responsible Party 4 | 5 | ```text 6 | Kaiser & Preusse 7 | Schwabentorring 10 8 | 79098 Freiburg 9 | Germany 10 | 11 | Phone: +49 163 77 39 234 12 | E-Mail: impressum@kaiser-preusse.com 13 | USt-ID: DE815832263 14 | ``` 15 | 16 | ## Responsible Person 17 | 18 | Responsible in the terms of § 6 MDStV: Martin Preusse 19 | 20 | ## Disclaimer of Liability 21 | 22 | Despite careful control we do not accept any liability for the content of external links. The content of linked web pages is subject to the sole responsibility of their respective operators. 23 | 24 | -------------------------------------------------------------------------------- /nodes_and_relationships.md: -------------------------------------------------------------------------------- 1 | # Nodes and Relationships in CovidGraph 2 | ## Format of this document 3 | - **Nodes** are declared as `:Nodelabel` (starting with ":" colon followed by a capital letter) 4 | - **Realtationships** are declared as `:RELATIONSHIP_NAME` (all capital letters) 5 | - **Properties** are declared as `propertyName` (starting with small letter) 6 | - Graph patterns are presented in Cypher query language style 7 | -- Example 1: node1 connected to node2 via relationship, 8 | **(node1:Nodelabel)-[relationship:RELATIONSHIP_TYPE]->(node2:Nodelabel)** 9 | -- Example 2: node connected to itself 10 | **(node1)-[r:RELATIONSHIP]->(node1)** 11 | 12 | 13 | ## Node labels in CovidGraph 14 | 15 | ### Literature information 16 | - `:PubMedArticle` represents a scientific article in the PubMed database. 17 | - `ArticleTitle` 18 | - `PMID` 19 | - `PublicationType` 20 | - `:ArticleId` of a PubMedArticle 21 | - `ArticleId.ID` 22 | - `:Abstract` of a PubMedArticle 23 | - `:AbstractText` stores text of an `:Abstract` as String 24 | - `text` 25 | - `:Author` of one or many PubMedArticle 26 | - `ForeName` 27 | - `Initials` 28 | - `LastName` 29 | - `:Affiliation` `Affiliation` of an authors 30 | - `Affiliation` 31 | - `:Contribution` is the link between combination of author, PubMedArticle and the author's affiliations 32 | - `:Journal` that published one or many PubMedArticle 33 | - `Title` 34 | - `ISOAbbreviation` 35 | - `:DZDPubMedArticle` Second label on `PubMedArticle` which for DZD article 36 | - `ArticleTitle` 37 | - `PMID` 38 | - `PublicationType` 39 | - `:DZDAuthor` Second label on `Author` for DZD scientists 40 | - `ForeName` 41 | - `Initials` 42 | - `LastName` 43 | - `:DZDAuthorNotationsHub` standardized names for DZDAuthor connecting all 44 | author spellings 45 | - `ForeName` 46 | - `Initials` 47 | - `LastName` 48 | - `:DZDAcademy` academy of the DZD, self explanary 49 | - `AcademyName` 50 | - `:DZDInstitute` institutes of the DZD, self explanary 51 | - `InstituteName` 52 | - `:DZDAffiliation` affiliations of the DZD, self explanary 53 | - `Affiliation` 54 | - `:DZDContribution` Second label on `:Contribution` 55 | - `:MeshQualifier` stores MeSH-Term 56 | - `text` 57 | - `:MeshDescriptor` stores MeSH-Term 58 | - `text` 59 | - `:MeshHeading` connects MeshQualifier and MeshDescriptor 60 | - `:MeshHeadingList` list of MeSH headings 61 | - `:Keyword` stores keyword of a PubMedArticle 62 | - `Keyword` 63 | - `:KeywordList` 64 | - `:Date` of a PubMedArticle 65 | - `Day` 66 | - `Month` 67 | - `Year` 68 | - `:MedlineJournalInfo` stores the iso abbreviation of a Journal 69 | - `MedlineTA` 70 | - `:Reference` of a PubMedArticle 71 | - `Citation` 72 | - `:ChemicalList` list of chemicals in a PubMedArticle 73 | - `:ReferenceList` list of references of a PubMedArticle 74 | - `:ISSN` of a PubMedArticle 75 | - `:JournalIssue` of a scientific journal 76 | - `Issue` 77 | - `Volume` 78 | - `:Identifier` from `source`: ORCID, ISNI, GRID and RINGGOLD 79 | - `ID` 80 | - `Source` 81 | - `:GrantList` list of grants of a PubMedArticle 82 | - `:Grant` of a PubMedArticle 83 | - `:PersonalNameSubjectList` self explanary 84 | - `:PersonalNameSubject` PubMedArticles that are Autobiographies 85 | - `:Investigator` PI of a PubMedArticle 86 | - `:GeneralNote` not yet investigated 87 | - `:Chemical` used in a PubMedArticle 88 | ### Molecular entities information 89 | - `:Gene` from Ensembl holds information of a gene in `sid` and `name` 90 | - `source` 91 | - `sid` 92 | - `name` 93 | - `taxid` 94 | - `:Transcript` are RNAs from coding genes, from `source`=RefSeq 95 | - `source` 96 | - `sid` 97 | - `taxid` 98 | - `:Protein` from `source`=Uniprot, RefSeq, Swissprot stores protein information 99 | - `source` 100 | - `sid` 101 | - `taxid` 102 | - `:Lipid` from `source`=SwissLipid 103 | - `:Metabolite` from `source`=HNGC 104 | - `name` 105 | - `definition` 106 | - `sid` 107 | - `taxid` 108 | - `source` 109 | - `:Pathway` molecular pathway from `source`=Reactome 110 | - `name` 111 | - `sid` 112 | - `taxid` 113 | - `source` 114 | - `org` Organism 115 | - `:SNP` SingleNucleotidePolymorphism/gene varition from `source`=GWAS which is associated with a `:Trait` and a `:Gene` 116 | - `snp_id` 117 | - `taxid` 118 | - `:Study` associated with a `:Trait` and a `:SNP` 119 | - `title` 120 | - `pubmedId` 121 | - `reported_genes` 122 | - `:Association` between `:Trait`, a `:SNP` and `:Study` 123 | - `:SNP_Interaction` of two or more `:SNP`s 124 | - `snp_id` 125 | - `taxid` 126 | - `:Trait` `name`is a feature or phenotype or disease which is connected to a gene variation `:SNP` 127 | - `name` 128 | - `:Phenotype` which is connected to a `:Gene` 129 | - `name` 130 | - `:GtexSample` specific sample of a `:GtexTissue` with raw data 131 | - `:GtexTissue` human tissue 132 | - `name` 133 | - `:GtexDetailedTissue` sub tissue of `:GtexTissue` and connected `:Gene` 134 | - `name` 135 | - `:MgiDescription` mouse gene description from `source`=MGI 136 | - `:GeneSymbolList` self explanatory 137 | 138 | 139 | ### Ontology information 140 | - `:Term` with `name` holds a term and is connected to its `:Ontology` 141 | - `name` 142 | - `definition` 143 | - `sid` 144 | - `:Disease` currently empty 145 | - `:Ontology` with `name`, connected to `:Term` and `source`=Obo FoundryCurrently from *Disease Ontology*, *GeneOntology*, *Mouse Phenotype Ontology* 146 | - `sid` 147 | - `:OntologySubset` self explanatory 148 | - `:SynonymTerm` 149 | 150 | ### Technical information 151 | - `:_FulltextLookUpDone_Label_property` Second label for node label. Combination of label and property was looked up on the fulltext index in abstracts 152 | - `:_FulltextLookUpDone_Protein_name` 153 | - `:_FulltextLookUpDone_Protein_sid` 154 | - `:OmitInMatch` second label for genes, and proteins that are omitted for text search 155 | - `:OmitSpecialChar` second label for genes and proteins because of special characters in name 156 | - `:OmitLengthOne` second label for genes and proteins because the name is only one character 157 | - `:OmitWord` second label for genes and proteins, because the name is an English word 158 | - `:_PipelineLogNode` technical information to one pipeline module 159 | - `:_PubMedXmlLoadingLog` technical information for data integration of one XML file from PubMed 160 | - `:_PipelineLogRun` technical information of the central pipeline run 161 | - `:ProteinSearch` intermediate label for proteins that need preprocessing steps before search 162 | - `:Word` list of words that are omitted in text search 163 | 164 | -------------------------------------------------------------------------------- /privacy-policy.md: -------------------------------------------------------------------------------- 1 | # privacy-policy 2 | 3 | CovidGraph is a non-profit collaboration of researchers, software developers, data scientists and medical professionals. This privacy policy will explain how our organization uses the personal data we collect from you when you use our website. 4 | 5 | ## Topics: 6 | 7 | * What data do we collect? 8 | * How do we collect your data? 9 | * How will we use your data? 10 | * How do we store your data? 11 | * Marketing 12 | * What are your data protection rights? 13 | * What are cookies? 14 | * How do we use cookies? 15 | * What types of cookies do we use? 16 | * How to manage your cookies 17 | * Privacy policies of other websites 18 | * Changes to our privacy policy 19 | * How to contact us 20 | * How to contact the appropriate authorities 21 | 22 | ## What data do we collect? 23 | 24 | CovidGraph collects the following data: 25 | 26 | * Personal identification information \(Name, email address, interest/profession group\) 27 | 28 | ## How do we collect your data? 29 | 30 | You directly provide CovidGraph with most of the data we collect. We collect data and process data when you: 31 | 32 | * Register online for any of our products or services. 33 | * Voluntarily use a contact form or provide feedback on any of our message boards or via email. 34 | * Use or view our website via your browser’s cookies. 35 | 36 | CovidGraph may also receive your data indirectly from the following sources: 37 | 38 | * LinkedIn 39 | * Twitter 40 | 41 | ## How will we use your data? 42 | 43 | CovidGraph collects your data so that we can: 44 | 45 | * Process your contact request. 46 | * Email you further information about the project. 47 | 48 | If you agree, CovidGraph will share your data with our partner companies so that they may offer you their products and services. 49 | 50 | * Kaiser & Preusse, German Center of Diabetes Research, Neo4j, PRODYNA, Structr GmbH and others. See [https://covidgraph.org/\#who-we-are](https://covidgraph.org/#who-we-are) for the full list of CovidGraph partners. 51 | 52 | When CovidGraph processes your contact request, it may send your data to a server. 53 | 54 | ## How do we store your data? 55 | 56 | CovidGraph securely stores your data in a database with the sole purpose of processing your contact request. 57 | 58 | CovidGraph will keep your personal data for 30 days. Once this time period has expired, we will delete your data by deleting your contact request from the database. 59 | 60 | If you no longer wish to be contacted for marketing purposes, please click here. What are your data protection rights? 61 | 62 | CovidGraph would like to make sure you are fully aware of all of your data protection rights. Every user is entitled to the following: 63 | 64 | The right to access – You have the right to request copies of your personal data from CovidGraph. We may charge you a small fee for this service. 65 | 66 | The right to rectification – You have the right to request that CovidGraph correct any information you believe is inaccurate. You also have the right to request that CovidGraph update the information you believe is incomplete. 67 | 68 | The right to erasure – You have the right to request that CovidGraph erase your personal data, under certain conditions. 69 | 70 | The right to restrict processing – You have the right to request that CovidGraph restricts the processing of your personal data, under certain conditions. 71 | 72 | The right to object to processing – You have the right to object to CovidGraph’s processing of your personal data, under certain conditions. 73 | 74 | The right to data portability – You have the right to request that CovidGraph transfer the data that we have collected to another organization, or directly to you, under certain conditions. 75 | 76 | If you make a request, we have one month to respond to you. If you would like to exercise any of these rights, please contact us at our email: 77 | 78 | ## Cookies 79 | 80 | Cookies are small files containing specific information relating to the way you view our website. These are downloaded onto the device used for that purpose. Depending on the type of cookie, they can be read, updated or deleted by the same servers. Some cookies are session specific and get deleted automatically when you leave the website. Others, such as those used for tracking or authentication, may be saved for longer. Some of these cookies are put in place by us whilst others are put in place by third parties. 81 | 82 | For further information, including how to disable and/or remove cookies visit allaboutcookies.org. 83 | 84 | ## How do we use cookies? 85 | 86 | CovidGraph uses cookies in a range of ways to improve your experience on our website, including: 87 | 88 | * Keeping you signed in 89 | * Understanding how you use our website 90 | 91 | What types of cookies do we use? 92 | 93 | There are a number of different types of cookies, however, our website uses: 94 | 95 | * Functionality – CovidGraph uses these cookies so that we recognize you on our website and remember your previously selected preferences. These could include what language you prefer and location you are in. A mix of first-party and third-party cookies are used. 96 | * Advertising – CovidGraph uses these cookies to collect information about your visit to our website, the content you viewed, the links you followed and information about your browser, device, and your IP address. CovidGraph sometimes shares some limited aspects of this data with third parties for advertising purposes. We may also share online data collected through cookies with our advertising partners. This means that when you visit another website, you may be shown advertising based on your browsing patterns on our website. 97 | 98 | ## How to manage cookies 99 | 100 | You can set your browser not to accept cookies, and allaboutcookies.org tells you how to remove cookies from your browser. However, in a few cases, some of our website features may not function as a result. Privacy policies of other websites 101 | 102 | The CovidGraph website contains links to other websites. Our privacy policy applies only to our website, so if you click on a link to another website, you should read their privacy policy. Changes to our privacy policy 103 | 104 | CovidGraph keeps its privacy policy under regular review and places any updates on this web page. This privacy policy was last updated on 28 April 2020. How to contact us 105 | 106 | If you have any questions about CovidGraph’s privacy policy, the data we hold on you, or you would like to exercise one of your data protection rights, please do not hesitate to contact us. 107 | 108 | Email us at: impressum@kaiser-preusse.com 109 | 110 | Call us: +49 1637739234 111 | 112 | Or write to us at: 113 | 114 | Kaiser & Preusse Schwabentorring 10 79098 Freiburg Germany 115 | 116 | ## How to contact the appropriate authority 117 | 118 | Should you wish to report a complaint or if you feel that CovidGraph has not addressed your concern in a satisfactory manner, you may contact the Information Commissioner’s Office. 119 | 120 | Email: impressum@kaiser-preusse.com 121 | 122 | Address: 123 | 124 | Kaiser & Preusse Schwabentorring 10 79098 Freiburg Germany 125 | 126 | -------------------------------------------------------------------------------- /screenshots/accessing-data/connect_neo4j_browser.png: 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/website/README.md: -------------------------------------------------------------------------------- 1 | # website 2 | 3 | -------------------------------------------------------------------------------- /website/content/English Edit - PR-03-20-Neo4j-COVID-19: -------------------------------------------------------------------------------- 1 | COVID*Graph provides researchers with the latest scientific data in a Knowledge Graph 2 | 3 | Munich, April 3, 2020 - Scientists, developers and data scientists are becoming increasingly involved in the fight against the COVID-19 pandemic. The COVID*Graph project is actively developing a Knowledge Graph to provide researchers with free and easy access to the latest research data. The aim is to gain important insights into the spread and course of the coronavirus as quickly as possible, and thus come one step closer to developing a vaccine. 4 | 5 | The not-for-profit project was launched in early March with the support of the German Center for Diabetes Research (DZD), Kaiser&Preusse, PRODYNA, Structr and yWorks, among others. The COVID*Graph team uses the graph database Neo4j to bundle scientific publications and research work in a central COVID-19 knowledge hub. Publicly available data sources on the coronavirus are linked with current and existing relevant patent specifications as well as data sets from genome and molecular biology databases. Currently, the Knowledge Graph comprises more than 16 million nodes and over 65 million edges, with the database growing each day. 6 | 7 | "In recent months, a great deal has been published very quickly about the coronavirus. The COVID-19 Open Research Database (CORD-19) alone stands at 44,000 scientific articles. It is difficult for medical research to keep an overview - especially since there hasn't been time to validate the work in the usual way," explains Dr. Alexander Jarasch, Head of Bioinformatics and Data Management at the German Center for Diabetes Research and co-initiator of COVID*Graph. "With our project we want to help researchers and scientists to find a quick and uncomplicated way through the vast amount of information. Therefore we are also happy about every form of support and cooperation". 8 | 9 | Knowledge graphs are semantic knowledge databases in which a large amount of heterogeneous data from different sources can be stored, linked and queried. The intuitive model consisting of nodes and edges makes it possible to illuminate collected knowledge clearly, to uncover connections and to recognize patterns. "The COVID*Graph provides the data basis for understanding the processes involved in a coronavirus infection. Why is this virus so contagious? And why do such severe complications occur? Linking large data sets and evaluating them provides new insights and provides researchers with approaches and hypotheses for their further research work," explains Dr. Martin Preusse, founder of Kaiser&Preusse, who co-initiated the COVID*Graph project. 10 | 11 | Invitation to the Neo4j initiative "Graphs4Good" 12 | 13 | Graph databases are widely used in data analysis - in medical research and drug development as well as in supply chain management and logistics. In the fight against COVID-19, graph analytics can be used, for example, to detect contacts of infected people (clusters). Shortest path algorithms can also be used to trace infection paths across multiple contact points, and to determine optimal supply chains and transport routes. The number of graph-based projects in the graph community has increased significantly since the outbreak of the pandemic. In addition to scientific projects such as COVID*Graph, these include smaller initiatives that, for example, help risk groups at a local level or help businesses and companies. 14 | 15 | For this reason, Neo4j has included all COVID-19 relevant graph projects in its "Graphs4Good" program. Users who use graph technology in the fight against the corona virus will receive free access to the enterprise version of the Neo4j database on request. In addition, Neo4j offers help in finding mentors, sharing datasets and exchanging information within the community. Data scientists, developers, researchers, graph enthusiasts and tech-interested people are also invited to participate in the virtual Graphs4Good Hackathon. 16 | -------------------------------------------------------------------------------- /website/content/README.md: -------------------------------------------------------------------------------- 1 | # content 2 | 3 | -------------------------------------------------------------------------------- /website/content/datasources.md: -------------------------------------------------------------------------------- 1 | # datasources 2 | 3 | ## COVID-19 Open Research Dataset \(CORD-19\) 4 | 5 | Collection of COVID-19 related scientific papers with metadata like authors, affiliations, references. 6 | 7 | Source of the dataset: [https://pages.semanticscholar.org/coronavirus-research](https://pages.semanticscholar.org/coronavirus-research) 8 | 9 | Kaggle challenge: [https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) 10 | 11 | ## Covid-19 cases from John Hopkins University 12 | 13 | John Hopkins University \(JHU\) aggregates data from WHO and other health organizations in a daily report. It contains the number of confirmed cases, deaths and recovered patients. 14 | 15 | Dashboard: [https://coronavirus.jhu.edu/map.html](https://coronavirus.jhu.edu/map.html) 16 | 17 | Data: [https://github.com/CSSEGISandData/COVID-19](https://github.com/CSSEGISandData/COVID-19) 18 | 19 | ## Population data from the UN 20 | 21 | The UN gathers data on world population statistics and publishes the world population prospects: [https://population.un.org/wpp/](https://population.un.org/wpp/) 22 | 23 | The latest data set in CSV format can be found here: [https://population.un.org/wpp/Download/Standard/CSV/](https://population.un.org/wpp/Download/Standard/CSV/) 24 | 25 | --------------------------------------------------------------------------------