192 Matching Annotations
  1. Last 7 days
  2. Apr 2022
    1. Dr Duncan Robertson [@Dr_D_Robertson]. (2021, October 29). ONS Covid survey. 2% of the population +ve. “The percentage of people testing positive for COVID-19 increased for all age groups, except for those in school Year 12 to those aged 34 years, where the trend was uncertain in the week ending 22 October 2021” https://ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronaviruscovid19infectionsurveypilot/29october2021 https://t.co/1n9KVq6wDT [Tweet]. Twitter. https://twitter.com/Dr_D_Robertson/status/1454050450106376192

    1. A filing system is indefinitely expandable, rhizomatic (at any point of timeor space, one can always insert a new card); in contradistinction with the sequen-tial irreversibility of the pages of the notebook and of the book, its interiormobility allows for permanent reordering (for, even if there is no narrative conclu-sion of a diary, there is a last page of the notebook on which it is written: its pagesare numbered, like days on a calendar).

      Most writing systems and forms force a beginning and an end, they force a particular structure that is both finite and limiting. The card index (zettelkasten) may have a beginning—there's always a first note or card, but it never has to have an end unless one's ownership is so absolute it ends with the life of its author. There are an ever-increasing number of ways to order a card index, though some try to get around this to create some artificial stability by numbering or specifically ordering their cards. New ideas can be accepted into the index at a multitude of places and are always internally mobile and re-orderable.

      link to Luhmann's works on describing this sort of rhizomatic behavior of his zettelkasten


      Within a network model framing for a zettelkasten, one might define thinking as traversing a graph of idea nodes in a particular order. Alternately it might also include randomly juxtaposing cards and creating links between ones which have similarities. Which of these modes of thinking has a higher order? Which creates more value? Which requires more work?

    1. The latest advances in machine learning — namely transformers and self-supervised learning in natural language processing — will also make it possible to build personalized discovery engines that organize and surface information that is timely, relevant, and impactful

      And possibly summarize it. And connect it to your own knowledge graph.

      Readwise's spaced repetition and integration with Roam/Obsidian is doing some cool stuff with surfacing connections and serendipitous reminders. Here is a Twitter thread I wrote to myself when I started playing with it: https://twitter.com/alexbowe/status/1476817961897783296

  3. Mar 2022
  4. Feb 2022
    1. It should be recognized that these basic note types are very different than the digital garden framing of 📤 (seedbox), 🌱 (seedling), 🪴 (sapling), 🌲 (evergreen), etc. which are another measure of the growth and expansion of not just one particular idea but potentially multiple ideas over time. These are a project management sort of tool for focusing on the growth of ideas. Within some tools, one might also use graph views and interconnectedness as means of charting this same sort of growth.

      Sönke Ahrens' framing of fleeting note, literature note, and permanent note are a value assignation to the types of each of these notes with respect to generating new ideas and writing.

  5. Jan 2022
    1. https://www.youtube.com/watch?v=z3Tvjf0buc8

      graph thinking

      • intuitive
      • speed, agility
      • adaptability

      ; graph thinking : focuses on relationships to turn data into information and uses patterns to find meaning

      property graph data model

      • relationships (connectors with verbs which can have properties)
      • nodes (have names and can have properties)

      Examples:

      • Purchase recommendations for products in real time
      • Fraud detection

      Use for dependency analysis

  6. Dec 2021
  7. Nov 2021
    1. we could look at at these sort of transitions in a sort of a two-dimensional uh graph in a sense and so we can start out and say okay groups can have more or 00:09:22 less conflict within them and groups can have more or less cooperation occurring within them and so if they are 00:09:34 down here in the left hand lower quadrant you basically are looking at more or less individuals so competitors so conflict not so much cooperation 00:09:48 if you move to the right hand side you start to form simple groups again individuals may come together to reap certain benefits and these benefits can be as simple as sort of 00:10:01 a selfish herd reducing predator risk predation risk and so on so not necessarily a lot of overt cooperation not necessarily a lot of 00:10:14 conflict going on then as you move to the upper left-hand quadrant you have groups that are now societies in other words there there might be rules as to who belongs 00:10:27 to the group uh there might be more cooperation within that within that group but also more conflict in the sense that the cooperation is producing benefits 00:10:38 and there may be conflicts over who is required to actually produce the benefits and how those benefits are actually shared within that group and then finally 00:10:49 uh if you can reduce that conflict uh such that everyone everyone more or less cooperates and doesn't doesn't there's the in any senses conflict with each other you can 00:11:02 actually turn the group into or the society into a coherent uh single organism at which point you may go back and start the whole process again

      Situatedness of modern human societies within this two dimensional graph is interesting. Although the images shown are of multi-cellular organisms, it can equally apply to smaller living units such as autonomously living genes, mitochondria or eukaryotes.

    1. In this proposed system, tags are full-fledged standalone files. They can be published and discussed just like any traditional heavyweight file can.

      Tags are first class

    2. The solution is to create a tag file that points to the original and edited photo, like DerivedWork(original=(some hash), derived=(some hash)).

      Relational tags

  8. Oct 2021
    1. Great teams have a plan to win when -- surprise, surprise -- they learn that a dozen other teams are pursuing their previously-thought-to-be-unique idea. They persevere when others (including us) tell them that ideas are cheap until they are brought to life. They both see themselves as unique and list many companies as their competitors.
    2. We believe more work in the future will look like that of software developers today (automating away tasks and harnessing the flexible power of computers to get work done)
    3. Soon we will see a one-person billion-dollar company, as many of the most talented individuals choose to work for themselves — as founders, in the creator economy, as freelancers, or in some other way.
    1. Coronavirus Pandemic Data Explorer. (n.d.). Our World in Data. Retrieved March 3, 2021, from https://ourworldindata.org/coronavirus-data-explorer

      is:webpage lang:en COVID-19 graph case death Germany Sweden UK Afghanistan Africa Albania Algeria Andorra Angola Anguilla Antigua Barbuda Argentina Armenia Asia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo Costa Rica Cote d'ivoire Croatia Cuba Cyprus Czechia Democratic Republic of Congo Denmark Djobouti Dominica Dominician Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Europe Europian Union Faeroe Islands Falkland Islands Fiji Finland France Gabon Gambia Georgia Ghana Gibraltar Greece Greenland Grenada Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Mashall Islands Mauritania Mauritius Mexico Micronesia Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria North America North Macedonia Northern Cyprus Norway Oceania Oman Pakistan Palestine Panama Papua New Guinea Paraguay Peru Philipines Poland Portugal Qatar Romania Russia Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Vincent Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South America South Korea South Sudan Spain Sri Lanka Sudan Suriname Switzerland Syria Taiwan Tajikistan Tanzania Thailand Timor Togo Trinidad Tobago Tunisia Turkey Turks and Caicos Islands Uganda Ukraine United Arab Emirates USA Uruguay Uzbekistan Vanuatu Vatican Venezuela Vietnam World Yemen Zambia Zimbabwe test vaccine chart map table data case fatality rate mortality

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  9. Sep 2021
    1. Active Indexers, Curators and Delegators can earn income from the network proportional to the amount of work they perform and their GRT stake.
    2. Curators are subgraph developers, data consumers or community members who signal to Indexers which APIs should be indexed by The Graph Network. Curators deposit GRT into a bonding curve to signal on a specific subgraph and earn a portion of query fees for the subgraphs they signal on; incentivizing the highest quality data sources. Curators will curate on subgraphs and deposit GRT via the Graph Explorer dApp. Because this occurs on a bonding curve, that means that the earlier you signal on a subgraph, the greater share of the query fees you earn on that subgraph for a given amount of GRT deposited. This also means that when you go to withdraw, you could end up with more or less GRT than you started with.

      cryptoeconomics still amazes me, how everything can be an opportunity for 'investment'

  10. Aug 2021
    1. In 1963, Ted Nelson coined the terms 'hypertext' and 'hypermedia' as part of a model he developed for creating and using linked content (first published reference 1965).[7] He later worked with Andries van Dam to develop the Hypertext Editing System (text editing) in 1967 at Brown University.
  11. Jul 2021
    1. Nick Holliman. (2021, May 30). @anthonybmasters @d_spiegel A quick visual summary of data on this week’s article by @anthonybmasters & @d_spiegel The outlook is uncertain, although we have survived a variant once already (B.1.1.7). The data on the effect of variants is analysed as fast as it (reliably) arrives. Https://t.co/vOKmCxYMGT https://t.co/3ZeJJTdRs3 [Tweet]. @binocularity. https://twitter.com/binocularity/status/1398957348492918784

    1. Alvin. (2021, July 8). An Estimated 279,000 Deaths & up to 1.25 Million Hospitalizations Averted by U.S. #COVID19 Vaccination Campaign (@commonwealthfnd analysis) 👉 Interpretation: #VaccinesWork Link: Https://t.co/0m8tq3In4f @Alison_Galvani @EricSchneiderMD @Vaccinologist @V2019N #SARSCoV2 https://t.co/SwaWxFnJ2H [Tweet]. @alvie_barr. https://twitter.com/alvie_barr/status/1413150922356654088

  12. Jun 2021
  13. May 2021
    1. Dr. Tom Frieden. (2021, April 30). Globally, the end of the pandemic isn’t near. More than a million lives depend on improving our response quickly. Don’t be blinded by the light at the end of the tunnel. There isn’t enough vaccine and the virus is gathering strength & speed. Global cooperation is crucial. 1/ [Tweet]. @DrTomFrieden. https://twitter.com/DrTomFrieden/status/1388172436999376899

    1. David Benkeser. (2020, November 9). Another view on uncertainty associated based on Pfizer’s results. Even if you were highly skeptical about MRNA vaccines (many are [were?]) with 50% prior belief that VE ~ 0, based on an 8:86 vax:placebo case split, the posterior probability that VE > 75% is ~ 1. Https://t.co/xtBONtGHmT [Tweet]. @biosbenk. https://twitter.com/biosbenk/status/1325856366225993729

    1. (((Howard Forman))). (2020, November 24). Truly good news out of #Italy. And we can all use it. Cases (23K), positive rate (12.3%), and hospitalizations all DOWN. ICU occupancy with smallest increase in months. Deaths (not surprisingly) the one exception with 3rd highest total. Https://t.co/YFh5nd2AXX [Tweet]. @thehowie. https://twitter.com/thehowie/status/1331311384626388994

  14. Mar 2021
    1. In a broader sense, taxonomy also applies to relationship schemes other than parent-child hierarchies, such as network structures. Taxonomies may then include a single child with multi-parents, for example, "Car" might appear with both parents "Vehicle" and "Steel Mechanisms"
    1. graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects
    1. Erich Neuwirth. (2020, November 11). #COVID19 #COVID19at https://t.co/9uudp013px Zu meinem heutigen Bericht sind Vorbemerkungen notwendig. Das EMS - aus dem kommen die Daten über positive Tests—Hat anscheinend ziemliche Probleme. Heute wurden viele Fälle nachgemeldet. In Wien gab es laut diesem [Tweet]. @neuwirthe. https://twitter.com/neuwirthe/status/1326556742113746950

  15. Feb 2021
    1. Miro Weinberger. (2020, December 3). Our 1st Covid-19 wastewater tests since Thanksgiving just came in—Virus levels are up significantly citywide. I hope that all of #BTV will look at this graph and see what I see: A call to action, to stop gathering with other households, and to get tested ASAP if you have https://t.co/8nxTwOOcFA [Tweet]. @MiroBTV. https://twitter.com/MiroBTV/status/1334613511692017664

    1. Thomas Van Boeckel. (2020, November 30). Https://t.co/s7o808PE3U now shows the ‘ad-hoc’ bed capacity as well as the bed capacity certified by the Swiss Society of Intensive Care Medicine. Data from partners the Coordinated Sanitary Service of @vbs_ddps. Thanks @nico_criscuolo @ChengZhao20, PhDs at @ETH_en https://t.co/5XxTexVyy9 [Tweet]. @thvanboeckel. https://twitter.com/thvanboeckel/status/1333323133592408064

  16. Jan 2021
    1. In fact, such small effectively closed scientific communities built on interpersonal relationships already exist to some extent

      so the weights in the reputation graph are personal knowledge, not citations or whatever.

  17. Dec 2020
    1. Usually while writing a Notion, I show the graph of how it connects to other Notions/Notes alongside it. I set the graph to show not only the 1st level links, as that only shows the links already apparent from the text I have in front of me. I set it to show 3 steps out at the start, and reduce to two steps when there are more links.

      This is a great idea that hasn't occurred to me before. When looking for non-obvious relationships between concepts (something that I think forms part of creativity), it makes sense to have the graph view open alongside the note you're working on.

  18. Nov 2020
    1. I've spent the last 3.5 years building a platform for "information applications". The key observation which prompted this was that hierarchical file systems didn't work well for organising information within an organisation.However, hierarchy itself is still incredibly valuable. People think in terms of hierarchies - it's just that they think in terms of multiple hierarchies and an item will almost always belong in more than one place in those hierarchies.If you allow users to describe items in the way which makes sense to them, and then search and browse by any of the terms they've used, then you've eliminated almost all the frustrations of a file system. In my experience of working with people building complex information applications, you need: * deep hierarchy for classifying things * shallow hierarchy for noting relationships (eg "parent company") * multi-values for every single field * controlled values (in our case by linking to other items wherever possible) Unfortunately, none of this stuff is done well by existing database systems. Which was annoying, because I had to write an object store.

      Impressed by this comment. It foreshadows what Roam would become:

      • People think in terms of items belonging to multiple hierarchies
      • If you allow users to describe items in a way that makes sense to them and allow them to search and browse by any of the terms they've used, you've solved many of the problems of existing file systems

      What you need to build a complex information system is:

      • Deep hierarchies for classifying things (overlapping hierarchies should be possible)
      • Shallow hierarchies for noting relationships (Roam does this with a flat structure)
      • Multi-values for every single field
      • Controlled values (e.g. linking to other items when possible)
    1. Knowledge graphs combine characteristics of several data management paradigms: Database, because the data can be explored via structured queries; Graph, because they can be analyzed as any other network data structure; Knowledge base, because they bear formal semantics, which can be used to interpret the data and infer new facts.

      Characteristics / benefits of a knowledge graph

    1. The ontology data model can be applied to a set of individual facts to create a knowledge graph – a collection of entities, where the types and the relationships between them are expressed by nodes and edges between these nodes, By describing the structure of the knowledge in a domain, the ontology sets the stage for the knowledge graph to capture the data in it.

      How ontologies and knowledge graphs relate.

    1. An ontology is as a formal, explicit specification of a sharedconceptualization that is characterized by high semantic ex-pressiveness required for increased complexity [9]. Ontolog-ical representations allow semantic modeling of knowledge,and are therefore commonly used as knowledge bases in artifi-cial intelligence (AI) applications, for example, in the contextof knowledge-based systems. Application of an ontology asknowledge base facilitates validation of semantic relationshipsand derivation of conclusions from known facts for inference(i.e., reasoning) [9]

      Definition of an ontology

    2. A knowledge graph acquires and integrates infor-mation into an ontology and applies a reasonerto derive new knowledge.

      Definition of a Knowledge Graph

    1. Maybe your dbt models depend on source data tables that are populated by Stitch ingest, or by heavy transform jobs running in Spark. Maybe the tables your models build are depended on by analysts building reports in Mode, or ML engineers running experiments using Jupyter notebooks. Whether you’re a full-stack practitioner or a specialized platform team, you’ve probably felt the pain of trying to track dependencies across technologies and concerns. You need an orchestrator.Dagster lets you embed dbt into a wider orchestration graph.

      It can be common for [[data models]] to rely on other sources - where something like [[Dagster]] fits in - is allowing your dbt fit into a wider [[orchestration graph]]

  19. Oct 2020
    1. The needs: keyword enables executing jobs out-of-order, allowing you to implement a directed acyclic graph in your .gitlab-ci.yml. This lets you run some jobs without waiting for other ones, disregarding stage ordering so you can have multiple stages running concurrently.
    1. A spreadsheet may be represented as a directed acyclic graph, with each cell a vertex and an edge connected a cell when a formula references another cell. Other applications include scheduling, circuit design and Bayesian networks.
    1. the name of something and when you press the button to go to the link if it wasn't there it made the card

      This is a phenomenally important UX insight and affordance that has become a foundation of how all modern wiki-linking knowledge graph tools work today. Kudos to Ward for this!