import {Plot} from "@mkfreeman/plot-tooltip"
import {map} from "@martien/ramda"

papers_r = FileAttachment("../data/all_papers.json").json()
n_columns = Object.entries(papers_r["doi"]).length
index_array = Array.from({length: n_columns}, (_, i) => i)
papers = map(i => map(x => x[i], papers_r), index_array)

my_papers = papers.filter(function(p) {
    return p.authors.map(a => a.full_name).includes("Joseph Rudzinski")
})

  import {Plot as Plot} from "@mkfreeman/plot-tooltip"

  import {map as map} from "@martien/ramda"

papers_r = Object {doi: Object, title: Object, publication_date: Object, authors: Object, citation_count: Object, bib_entry: Object, abstract: Object, tldr: Object, year: Object, journal.name: Object, journal.url: Object, journal.issue: Object, journal.abbreviation: Object, journal.pages: Object, journal.volume: Object, open_access.is_open_access: Object, open_access.open_access_status: Object, open_access.landing_page_url: Object, open_access.pdf_url: Object, embedding.x: Object, …}

n_columns = 82

index_array = Array(82) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, …]

papers = Array(82) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

my_papers = Array(12) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

Joseph Rudzinski

ORCID

Group publications

Inputs.table(my_papers, {
    columns: [
    "doi",
    "title",
    "journal.name",
    "journal.volume",
    "journal.issue",
    "journal.pages",
    "year",
    "embedding.topic_name"
  ],
  header: {
    "journal.name": "journal",
    "journal.volume": "volume",
    "journal.issue": "issue",
    "journal.pages": "pages",
    "embedding.topic_name": "topic",
  },
  format: {
    doi: doi => htl.html`<a href=https://doi.org/${doi} target=_blank>${doi}</a>`,
    title: title => htl.html`${title}`,
    "journal.volume": volume => htl.html`<b>${volume}</b>`,
    year: year => htl.html`${year}`,
  },
  width: {
    "title": 10
  },
  layout: "auto",
})

	doi	title	journal	volume	issue	pages	year	topic
	10.1021/acs.jcim.4c01754	Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations	Journal of Chemical Information and Modeling	64	24	9413-9423	2024	kinetic | model | simulation
	10.1063/5.0104914	Broad chemical transferability in structure-based coarse-graining	The Journal of Chemical Physics	157	10		2022	model | cg | coarsegrained
	10.1088/1361-648X/abe6e1	Dynamical properties across different coarse-grained models for ionic liquids	Journal of Physics: Condensed Matter	33	22	224001	2021	model | cg | coarsegrained
	10.1063/5.0031249	Coarse-grained conformational surface hopping: Methodology and transferability	The Journal of Chemical Physics	153	21		2020	system | md | reactions
	10.1088/2632-2153/ab80b7	Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders	Machine Learning: Science and Technology	1	1	015012	2020	kinetic | model | simulation
	10.1063/1.5131105	Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross correlations	The Journal of Chemical Physics	151	24		2019	model | cg | coarsegrained
	10.1063/1.5064808	Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics	The Journal of Chemical Physics	150	2		2019	kinetic | model | simulation
	10.1103/physrevlett.121.256002	Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics	Physical Review Letters	121	25		2018	system | md | reactions
	10.1063/1.5025125	Structural-kinetic-thermodynamic relationships identified from physics-based molecular simulation models	The Journal of Chemical Physics	148	20		2018	kinetic | model | simulation
	10.3390/computation6010021	The Role of Conformational Entropy in the Determination of Structural-Kinetic Relationships for Helix-Coil Transitions	Computation	6	1	21	2018	twostate | analysis | cooperativity
	10.1140/epjst/e2016-60114-5	Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fields	The European Physical Journal Special Topics	225	8-9	1373-1389	2016	kinetic | model | simulation
	10.1063/1.4941455	Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information	The Journal of Chemical Physics	144	5		2016	kinetic | model | simulation

Group collaborators

collaborators = Array.from(
    new Set(
        my_papers.flatMap(p => p.authors)
        .filter(function(a) {
            return a.full_name != "Joseph Rudzinski"
            }
        )
    )
)

list_of_collaborators = (
    Array.from(d3.group(collaborators, a => a.full_name).keys())
)
dict_of_collaborators = (
    collaborators.reduce(
        (acc, x) => (
            {...acc, [x.full_name]:
                {
                    "given": x.given,
                    "family": x.family,
                    "affiliation_city": x.affiliation.city,
                    "affiliation_name": x.affiliation.name,
                    "affiliation_country": x.affiliation.country,
                    "orcid": x.orcid,
                }
            }
        ),
        {}
    )
)

Inputs.table(
    list_of_collaborators.map(c => dict_of_collaborators[c]),
    {
        columns: [
            "given",
            "family",
            "orcid",
            "affiliation_country",
            "affiliation_city",
            "affiliation_name",
        ],
        header: {
            "affiliation_country": "country",
            "affiliation_city": "city",
            "affiliation_name": "affiliation",
        },
        multiple: false,
        sort: "family",
        layout: "fixed",
        format: {
            orcid: orcid =>
            htl.html`<a href=https://orcid.org/${orcid} target=_blank>${orcid}</a>`
        },
    }
)

collaborators = Array(24) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

list_of_collaborators = Array(10) ["Tristan Bereau", "Luis Walter", "Kiran Kanekal", "Sebastian Kloth", "Svenja Wörner", "Tamisra Pal", "Kurt Kremer", "Michael Vogel", "Yasemin Bozkurt Varolgüneş", "Marc Radu"]

dict_of_collaborators = Object {Tristan Bereau: Object, Luis Walter: Object, Kiran Kanekal: Object, Sebastian Kloth: Object, Svenja Wörner: Object, Tamisra Pal: Object, Kurt Kremer: Object, Michael Vogel: Object, Yasemin Bozkurt Varolgüneş: Object, Marc Radu: Object}

	given	▴family	orcid	country	city	affiliation
	Tristan	Bereau	0000-0001-9945-1271	DE	Heidelberg	Heidelberg University
	Yasemin	Bozkurt Varolgüneş
	Kiran	Kanekal	0000-0003-1785-5219
	Sebastian	Kloth	0009-0007-0130-0469	DE	Darmstadt	Technical University of Darmstadt
	Kurt	Kremer	0000-0003-1842-9369
	Tamisra	Pal	0000-0002-8613-3431	IN	Kolkata	S.N. Bose National Centre for Basic Sciences
	Marc	Radu				Max Planck Institute for Polymer Research , Mainz 55128, Germany
	Michael	Vogel	0000-0003-2706-3522	DE	Darmstadt	TU Darmstadt
	Luis	Walter	0009-0000-0113-6215
	Svenja	Wörner