{
 "about": "The run log (the next step after the agentic research assistant): its five steps and the systems underneath them, from Shared knowledge for condensed matter physics (v0.1 draft). A proposal, not a finished system. \"view\" says what the lab, the reviewer and the public can see once a step is done; \"today\" is the same step as it works now.",
 "visibility": {
  "lab": "The lab",
  "reviewer": "The reviewer",
  "public": "The public"
 },
 "steps": [
  {
   "id": "w-log",
   "n": 1,
   "icon": "log",
   "name": "Log every run",
   "short": "Lab tools send it automatically",
   "does": "For a lab that opts in, its measurement software sends every run and its settings to a shared system of record, a tracked archive run by a third party. A run here is one measurement the software saves, such as a gate sweep, not a whole cooldown. The record is encrypted: the lab can see its own runs, and otherwise only the assistant, run on the record operator's systems, reads them. Nothing depends on someone remembering to upload, so failed runs are kept too. Someone still records which device each run came from, and devices that fail before measurement never produce a run.",
   "proves": "A full, time-stamped record of what was done.",
   "ai": "Connects to the lab's software, documents each step and names files consistently.",
   "view": {
    "lab": "Every run",
    "reviewer": "",
    "public": ""
   },
   "today": {
    "name": "Data on lab drives",
    "short": "Scattered across machines",
    "does": "Data sits on lab computers and students' drives. Failed runs are seldom written up, and data is often lost when people leave.",
    "view": {
     "lab": "What was kept",
     "reviewer": "",
     "public": ""
    }
   }
  },
  {
   "id": "w-mark",
   "n": 2,
   "icon": "mark",
   "name": "Mark the final data",
   "short": "Tag the runs used in the paper",
   "does": "The researcher tags the runs that go into the paper as final. Everything else stays in the log.",
   "proves": "Which data was selected, and out of how much.",
   "ai": "Tags and packages the final data with the analysis that turns it into figures.",
   "view": {
    "lab": "Every run, final data marked",
    "reviewer": "",
    "public": ""
   },
   "today": {
    "name": "Choose the figures",
    "short": "Often the clearest device",
    "does": "The team picks the data for the figures, often the clearest device and parameter range. The paper rarely says how many were measured.",
    "view": {
     "lab": "The selected data",
     "reviewer": "",
     "public": ""
    }
   }
  },
  {
   "id": "w-submit",
   "n": 3,
   "icon": "submit",
   "name": "Submit",
   "short": "Paper and final data; the log stays sealed",
   "does": "The paper goes to the journal with the final data and the analysis code, and the assistant runs on it, started by the researcher before or at submission, so the same results can go to every journal. The full log stays sealed in the record.",
   "proves": "Exactly which data the paper rests on.",
   "ai": "Builds the submission package.",
   "view": {
    "lab": "Everything",
    "reviewer": "Paper and final data",
    "public": ""
   },
   "today": {
    "name": "Submit the paper",
    "short": "The PDF and its figures",
    "does": "The paper goes to the journal with its figures. A referee who wants the data has to ask through the editors, which is unusual.",
    "view": {
     "lab": "What was kept",
     "reviewer": "The paper",
     "public": ""
    }
   }
  },
  {
   "id": "w-check",
   "n": 4,
   "icon": "check",
   "name": "Assistant checks, researcher answers",
   "short": "Issues go to the researcher first",
   "does": "The agentic research assistant, running as a fixed workflow inside the record, reviews the research against all the data. It reruns the figures from the final data and leaves code another lab can rerun, compares the final data with the full log (for example, 3 of 40 devices shown), and asks which known lookalikes were ruled out. It sends any issues to the researcher, who corrects each one or explains it. The reviewer gets the paper, the final data, the report and that conversation, but not the full log and not an open-ended agent. If something looks off, the reviewer sends questions back. The researcher answers each one or lets the AI answer from the data, and can decline a question only by giving a reason the reviewer sees; the editor settles disagreements. This raises the floor without setting a limit: a reviewer can still ask for more data, as today, and the researcher decides what to share. The check doesn't reject a paper on its own.",
   "proves": "The figures follow from the data, and the selection is explained.",
   "ai": "Reviews the research against a log too large for a person to read, checks the literature for known lookalikes, and holds the conversation with the researcher.",
   "view": {
    "lab": "Everything, plus the AI's questions",
    "reviewer": "Adds the AI report and conversation",
    "public": ""
   },
   "today": {
    "name": "Referee reads the PDF",
    "short": "Judges only what's shown",
    "does": "The referee judges the paper as written. Unpaid and short of time, they rarely rerun anything, and they can't see what wasn't shown.",
    "view": {
     "lab": "What was kept",
     "reviewer": "The paper",
     "public": ""
    }
   }
  },
  {
   "id": "w-default",
   "n": 5,
   "icon": "default",
   "name": "Make it the default",
   "short": "Once the field values it",
   "does": "Adoption starts optional: labs choose whether to log, researchers run the assistant before submitting, and journals offer it to authors and run it for referees. Once the field values it, as other fields came to value trial registration and structure deposition, journals and funders make the report and the log the default, so it's a favor to no one. The paper is published with a badge that says exactly what was checked: the figures were rerun from the final data, and every issue the check raised, including how the data was selected, was corrected or explained. It doesn't say the result is right. The lab decides whether to share the final data publicly.",
   "proves": "A floor the whole field shares.",
   "ai": "Keeps it cheap enough that making it the default is acceptable.",
   "view": {
    "lab": "Everything",
    "reviewer": "Paper, AI report and conversation",
    "public": "Paper, check badge, data if shared"
   },
   "today": {
    "name": "Publish",
    "short": "Data on request",
    "does": "The paper is published with a statement that data is available on request. Across fields, those requests succeed only part of the time.",
    "view": {
     "lab": "What was kept",
     "reviewer": "The paper",
     "public": "The paper"
    }
   }
  }
 ],
 "components": [
  {
   "id": "c-tools",
   "name": "Lab tools",
   "icon": "instrument",
   "zone": "lab",
   "status": "exists",
   "sees": "lab",
   "does": "The instruments, measurement software and analysis notebooks the lab already uses. Nothing here has to change.",
   "tools": [
    {
     "name": "QCoDeS",
     "url": "https://microsoft.github.io/Qcodes/dataset/introduction.html"
    },
    {
     "name": "Labber",
     "url": "https://www.keysight.com/us/en/products/software/application-sw/labber-software.html"
    },
    {
     "name": "PyMoDAQ",
     "url": "https://github.com/pymodaq/pymodaq"
    },
    {
     "name": "eLabFTW",
     "url": "https://doc.elabftw.net/"
    }
   ],
   "note": "Many quantum device labs already save each measurement with its instrument settings, but only to local storage; others use homegrown, LabVIEW or vendor software."
  },
  {
   "id": "c-connector",
   "name": "Connector",
   "icon": "connector",
   "zone": "lab",
   "status": "partly",
   "sees": "lab",
   "does": "Open-source software that sends each run and its settings to the system of record as it happens, through the tools' own interfaces. No manual upload, so nothing depends on someone remembering.",
   "tools": [
    {
     "name": "NOMAD API",
     "url": "https://nomad-lab.eu/prod/v1/docs/howto/programmatic/publish_python.html"
    },
    {
     "name": "Zenodo API",
     "url": "https://developers.zenodo.org/"
    },
    {
     "name": "Globus Flows",
     "url": "https://docs.globus.org/guides/recipes/instrument-transfer-and-share/"
    },
    {
     "name": "QCoDeS export",
     "url": "http://microsoft.github.io/Qcodes/examples/DataSet/Exporting-data-to-other-file-formats.html"
    }
   ],
   "note": "The interfaces exist. The glue between lab software and a shared record would be new."
  },
  {
   "id": "c-response",
   "name": "Researcher replies",
   "icon": "reply",
   "zone": "lab",
   "status": "new",
   "sees": "reviewer",
   "does": "The researcher corrects each issue the assistant raises or explains it, in a conversation the reviewer reads.",
   "tools": []
  },
  {
   "id": "c-tag",
   "name": "Final data tag",
   "icon": "mark",
   "zone": "record",
   "status": "new",
   "sees": "lab",
   "does": "Marks which runs the paper uses. It points into the full log, so the assistant can compare the two.",
   "tools": []
  },
  {
   "id": "c-record",
   "name": "System of record",
   "icon": "log",
   "zone": "record",
   "status": "new",
   "sees": "lab",
   "does": "One shared service, run by a neutral third party. Every run is stored encrypted, with a time stamp and a fingerprint, so nothing can be changed quietly later. The lab sees its own runs; otherwise only the assistant, run on the record operator's systems, reads them, and nobody can run ad hoc queries.",
   "tools": [
    {
     "name": "Zenodo",
     "url": "https://help.zenodo.org/docs/share/access-requests/"
    },
    {
     "name": "NOMAD",
     "url": "https://nomad-lab.eu/prod/v1/docs/tutorial/upload_publish.html"
    },
    {
     "name": "trusted time stamps (RFC 3161)",
     "url": "https://datatracker.ietf.org/doc/html/rfc3161"
    }
   ],
   "note": "Repositories, fingerprints and time stamps exist. A shared service that takes a continuous log of every run does not."
  },
  {
   "id": "c-package",
   "name": "Submission package",
   "icon": "package",
   "zone": "journal",
   "status": "partly",
   "sees": "reviewer",
   "does": "The paper, final data and analysis code, built automatically. The assistant runs from it, before or at submission.",
   "tools": [
    {
     "name": "Figshare at Nature Physics",
     "url": "https://www.nature.com/articles/s41567-023-02040-4"
    },
    {
     "name": "Code Ocean",
     "url": "https://www.nature.com/articles/s41567-025-02808-w"
    },
    {
     "name": "Dryad",
     "url": "https://datadryad.org/help/submission_steps/peer_review"
    }
   ]
  },
  {
   "id": "c-checker",
   "name": "Research assistant",
   "icon": "check",
   "zone": "record",
   "status": "new",
   "sees": "reviewer",
   "does": "For a lab that logs, the assistant also runs as a fixed workflow inside the record, hosted by the third party; the researcher can start it before submitting and send the results to any journal. Reviews the research against all the data: reruns the figures from the final data, compares the final data with the full log (for example, 3 of 40 devices shown), asks which known lookalikes were ruled out, and sends any issues to the researcher. Its report and the conversation go to the reviewer.",
   "tools": [
    {
     "name": "AEA Data Editor (people)",
     "url": "https://www.aeaweb.org/journals/data/faq"
    },
    {
     "name": "cascad (people)",
     "url": "https://www.cascad.tech/terms"
    },
    {
     "name": "Living Science (AI)",
     "url": "https://livingscience.ai/blog/introducing-living-science"
    }
   ],
   "note": "People rerun papers before publication in economics. AI reruns exist for social science and machine learning. Nothing yet compares final data with a full log."
  },
  {
   "id": "c-portal",
   "name": "Reviewer view",
   "icon": "portal",
   "zone": "journal",
   "status": "partly",
   "sees": "reviewer",
   "does": "Where the reviewer reads the paper, the assistant's report on issues and the conversation between the assistant and the researcher. The raw log stays sealed and there is no open-ended agent: the reviewer can send questions, which the researcher answers or lets the AI answer, and can decline only with a reason the reviewer sees.",
   "tools": [
    {
     "name": "Dryad private review links",
     "url": "https://datadryad.org/help/submission_steps/peer_review"
    },
    {
     "name": "NOMAD reviewer access",
     "url": "https://nomad-lab.eu/prod/v1/docs/tutorial/upload_publish.html"
    }
   ]
  },
  {
   "id": "c-public",
   "name": "Public record",
   "icon": "public",
   "zone": "public",
   "status": "partly",
   "sees": "public",
   "does": "The published paper with its check badge, which says what was checked rather than that the result is right, and the final data if the lab chooses to share it.",
   "tools": [
    {
     "name": "ACM badges",
     "url": "https://www.acm.org/publications/policies/artifact-review-and-badging-current"
    },
    {
     "name": "HEPData",
     "url": "https://www.hepdata.net/submission"
    }
   ]
  },
  {
   "id": "c-funder",
   "name": "Field view",
   "icon": "funder",
   "zone": "public",
   "status": "new",
   "sees": "public",
   "does": "A view of the whole field, built from the tags on published papers and public notes, never from the encrypted record: what's being worked on, what's been tried, and where nobody is investing. It helps funders see where money would do the most good. It shows topics, not a lab's data or check results.",
   "tools": []
  }
 ]
}