From 9705cb74e485293b94a4aac1281b74e751b3a229 Mon Sep 17 00:00:00 2001 From: David Mora Date: Fri, 5 Jan 2018 14:41:59 -0500 Subject: [PATCH] Add url to SemanticScholar page for each paper --- ...2-format-graph-data-from-authors-papers.py | 93 ++++++++++--------- data/graph-data.json | 2 +- js/index.js | 4 +- project-plans.txt | 79 ---------------- 4 files changed, 52 insertions(+), 126 deletions(-) delete mode 100644 project-plans.txt diff --git a/data/2-format-graph-data-from-authors-papers.py b/data/2-format-graph-data-from-authors-papers.py index fa91ace..964b2ea 100644 --- a/data/2-format-graph-data-from-authors-papers.py +++ b/data/2-format-graph-data-from-authors-papers.py @@ -1,15 +1,19 @@ import json from pprint import pprint + +papersDict = {} +coreAuthors = {} +glob = {"updatingIndex": 0, "parsingCoreAuthors": True } # keeps track of what index in the array you're adding to (used as "node descriptor" for links) + + # IMPORT JSON FILE -f = open("papers-by-title.json", "r") # 123 articles (each which contain their own citations, refences) -papersDict = json.loads(f.read()) -f.close() +with open("papers-by-title-SMALL.json", "r") as f: # articles (each which contain their own citations, refences) + papersDict = json.loads(f.read()) # RETRIEVE CORE AUTHORS -f = open("core-authors-list.json", "r") # 123 articles (each which contain their own citations, refences) -coreAuthors = json.loads(f.read()) -f.close() +with open("core-authors-list.json", "r") as f: + coreAuthors = json.loads(f.read()) # FOR SIMPLE TESTING PURPOSES: # papersDict = { "A-node": { @@ -29,48 +33,16 @@ # } # } -paperIdToIndex = {} # # paper title -> index +paperIdToIndex = {} # paper title -> index nodeDict = {} # paper index -> node object linkArray = [] def generateGraphData(): - glob = {"updatingIndex": 0, "parsingCoreAuthors": True } # keeps track of what index in the array you're adding to (used as "node descriptor" for links) - def nodeAdded(paper): - paperId = paper["paperId"] - # FILTER: for citations/refs, keep only if isInfluential - influential = paper.get('isInfluential', True) - if influential and paperId not in paperIdToIndex: - paperIdToIndex[paperId] = glob["updatingIndex"] - newNode = { - "title": paper["title"], - "year" : paper["year"], - "keyPhrases": [], - "index": glob["updatingIndex"], - "id": glob["updatingIndex"], - "paperId": paper["paperId"] - } - if glob["parsingCoreAuthors"]: - newNode["influentialCitationCount"] = paper["influentialCitationCount"] - authorsDict = {} - for authorObj in paper["authors"]: - authorsDict[authorObj["authorId"]] = authorObj["name"] # hash map for fast access when filtering - if authorObj["authorId"] in coreAuthors: - newNode["coreAuthor"] = authorObj["authorId"] # ISSUE: if two core authors on same paper, only list first as singular coreAuthor - newNode["authors"] = authorsDict - nodeDict[glob["updatingIndex"]] = newNode - glob["updatingIndex"] += 1 - return True - return influential - - def addEdge(source, target): - linkArray.append({"source": source, "target": target}) - # first add all papers authored by the core authors # which contain the highest resolution info for paperTitle in papersDict: paper = papersDict[paperTitle] - # add to node - nodeAdded(paper) + addNode(paper) # then add all the core authors ref/citations (overlap won't overwrite) glob["parsingCoreAuthors"] = False @@ -80,19 +52,19 @@ def addEdge(source, target): hubPaperIndex = paperIdToIndex[paper["paperId"]] # add all in going links (citations) for citedPaper in paper["citations"]: - nodeAdded(citedPaper) + addNode(citedPaper) paperId = citedPaper["paperId"] if paperId in paperIdToIndex and citedPaper.get("isInfluential", True): addEdge(paperIdToIndex[paperId], hubPaperIndex) # add all out going links (references) for refPaper in paper["references"]: - nodeAdded(refPaper) + addNode(refPaper) paperId = refPaper["paperId"] if paperId in paperIdToIndex and refPaper.get("isInfluential", True): addEdge(hubPaperIndex, paperIdToIndex[paperId]) - pprint(len(nodeDict)) - pprint(len(linkArray)) + print("Node count: " + str(len(nodeDict))) + print("Link count: " + str(len(linkArray))) # convert Dict to List nodeList = [None] * len(nodeDict) @@ -110,5 +82,38 @@ def addEdge(source, target): # for link in linkArray: # gd.write("'" + str(link["source"]) + "','" + str(link["target"]) + "'\n") # gd.close() + return + +# ---Helper functions--- + +def addNode(paper): + paperId = paper["paperId"] + # FILTER: for citations/refs, keep only if isInfluential + influential = paper.get('isInfluential', True) + if influential and paperId not in paperIdToIndex: + paperIdToIndex[paperId] = glob["updatingIndex"] + newNode = { + "title": paper["title"], + "year" : paper["year"], + "keyPhrases": [], + "index": glob["updatingIndex"], + "id": glob["updatingIndex"], + "paperId": paper["paperId"], + "linkToPaper": paper["url"] + } + if glob["parsingCoreAuthors"]: + newNode["influentialCitationCount"] = paper["influentialCitationCount"] + authorsDict = {} + for authorObj in paper["authors"]: + authorsDict[authorObj["authorId"]] = authorObj["name"] # hash map for fast access when filtering + if authorObj["authorId"] in coreAuthors: + newNode["coreAuthor"] = authorObj["authorId"] # ISSUE: if two core authors on same paper, only list first as singular coreAuthor + newNode["authors"] = authorsDict + nodeDict[glob["updatingIndex"]] = newNode + glob["updatingIndex"] += 1 + return + +def addEdge(source, target): + linkArray.append({"source": source, "target": target}) generateGraphData() \ No newline at end of file diff --git a/data/graph-data.json b/data/graph-data.json index c6be0b8..7c1f460 100644 --- a/data/graph-data.json +++ b/data/graph-data.json @@ -1 +1 @@ -{"nodes": [{"index": 0, "paperId": "ef52775276f83a46162a9b364335d9ee5ee73b99", "influentialCitationCount": 78, "title": "Learned helplessness in humans: critique and reformulation.", "authors": {"3084765": "Martin E. 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"target": 149}, {"source": 804, "target": 122}, {"source": 805, "target": 122}]} \ No newline at end of file diff --git a/js/index.js b/js/index.js index cc1c553..b5c43f5 100644 --- a/js/index.js +++ b/js/index.js @@ -159,7 +159,7 @@ const getCoreAuthorColor = (d) => d.coreAuthor ? authorColor[d.coreAuthor] : non const filterGraph = () => { nodes = graph.nodes.filter(node => shouldKeepNode(node)); links = graph.links.filter(link => shouldKeepLink(graph.nodesById, link)); - restart(); + updateVis(); } // Filter predicates @@ -195,7 +195,7 @@ const makeAuthorInactive = (authorId) => { } // 3. UPDATE GRAPH AFTER FILTERING DATA ------------------------------------------------------------------------- -function restart() { +function updateVis() { if(!simulation) { simulation = d3 .forceSimulation() diff --git a/project-plans.txt b/project-plans.txt deleted file mode 100644 index 9f67fab..0000000 --- a/project-plans.txt +++ /dev/null @@ -1,79 +0,0 @@ -DATA VIS PROJECT PLAN: -0. write a spec of what data I want, and what structures should house it -DATA I WANT: - -AUTHORS: -"Angela Lee Duckworth" -"Barbara L. Fredrickson" -"Carol S. Dweck" -"Corey Lee M. Keyes" -"Dacher J Keltner" -"Daniel J Siegel" -"Daniel T. Gilbert" -"Emma M Seppala" -"Frederic Luskin" -"Jennifer L. Aaker" -"Jon Kabat-Zinn" -"Kelly M McGonigal" -"Kristin D. Neff" -"Martin E. P. Seligman" -"Mihály Csíkszentmihályi" -"Richard J. Davidson" -"Robert A. Emmons" -"Sonja Lyubomirsky" -"Zindel V. Segal" - - -2. use out-of-the-box graph vis to get a sense, run some basic stats -- Try Gephi -- Think of a way to reduce cluttr, filter to most important nodes (then format data for easy use) -- Put it back in d3 - -FOCUS IN<<<<<<<<<<<< -What’s my goal right now? -What’s my main focus for exploring, visualizing the data? That should drive how I format it. -VALUES: -- sees changes, connections, growth happening over time -- connect papers to key phrases, areas -ACTION: - 1. Every node needs - a. an array of its key phrases (after DOWNLOAD FINISHES) -- remove clutter, show only what’s most important (this is an introduction tool, not a expert tool) - - -Problem: A clear statement of the problem your project addresses. -“scientific publication has been extended far beyond our present ability to make real use of the record.” --Vannevar Bush -Especially true for noobs without any prior context who face 2.5 million new papers each year. -Understanding of how research papers, authors, and applications fit together remains trapped inside the minds of experts — how do we give novices that sort of perspective on the field's "knowledge landscape" (without years of reading papers)? - - -Motivation: An explanation of why the problem is interesting and what makes it difficult to solve. -Young researchers and scholars core challenge is to understand an academic fields' larger context and ecosystem. -However, vitually all resources display information linearly, statically, and without context. -Further, little aggregate or associative data sources exist -- the few that do typically prove overly complex and cluttered. - -Approach: A description of the techniques or algorithms you used to solve the problem. -Selecting positive psychology as the field to focus on, I consulted several experts to settle on a list of 19 of the most core authors for new comers know. -I used Semantic Scholar's API to assemble meta data on every paper written, cited, or referenced by the 19 core authors. To reduce overwhelm, I filtered those further by including only papers which were highly influential on the field or other papers in the graph, as measured by Semantic Scholar. - -Results: Screenshots and a working demo of the system you built. - - -Future Work: An explanation of how the work could be extended. -Two major areas of testing in and optimising for exist: -1. Academic courses: -Might this be a useful tool for instructors, and how might they integrate it into their classes? -What are the core needs, questions, and uses of students? -2. Onboarding beginning researchers: -Particularly for undergrads starting in a lab, how might this tool give larger conceptual and methadological context and reference for day-to-day work? - - - - - - - - - -