mbjones

library(dataone)

# Download a CSV-formatted data file from the KNB 
# repository, and convert it to a data.frame
get_dataframe <- function(pid) {
  d1c <- D1Client("PROD", "urn:node:KNB")
  data_raw <- getObject(d1c@mn, pid)
  data_char <- rawToChar(data_raw)
  csv_text <- textConnection(data_char)
  df <- read.csv(csv_text, stringsAsFactors = FALSE)

mbjones

---
title: "abstracts"
author: "Matt Jones"
date: "10/11/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(vroom)

mbjones

---
title: "geopackage"
author: "Matt Jones"
date: "7/23/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

mbjones

{
    "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
    "@type": "SoftwareSourceCode",
    "license": "https://spdx.org/licenses/Apache-2.0",
    "codeRepository": "https://github.com/codemeta/codemeta",
    "dateCreated": "2020-02-04",
    "datePublished": "2020-02-05",
    "downloadUrl": "https://codemeta.github.io/",
    "name": "CodeMeta",
    "description": "CodeMeta is a metadata language for code.",

mbjones

---
title: "FAIR Metrics"
author: "Matt Jones"
date: "5/8/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)

mbjones

#!/bin/bash
WORKDIR=${PWD}

create_repos () {
    rm -rf repo-split-example repo-split-recent repo-split-history
    # Create the repo to be split
    example_repo
    
    # Create the repo to contain the historical commits
    HISTREPO="file://${WORKDIR}/repo-split-history"

mbjones

@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix ns0: <http://www.openarchives.org/ore/terms/> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix ns1: <http://purl.org/spar/cito/> .
@prefix dc11: <http://purl.org/dc/elements/1.1/> .

<https://cn-stage-2.test.dataone.org/cn/v2/resolve/urn%3Auuid%3A936436eb-2676-44b2-920c-acc5e28384f9>
  prov:wasGeneratedBy <urn:uuid:aabccb0e-ccc8-4db8-a61d-53eb314906be> ;
  a <http://purl.dataone.org/provone/2015/01/15/ontology#Data> ;

mbjones

#!/usr/bin/ruby

# For an OO language, this is distinctly procedural. Should probably fix that.
require 'json'

details = Hash.new({})

capture_params = [
  { :name => "title", :message => "Enter project name." },
  { :name => "url", :message => "Enter the URL of the project repository." },

mbjones

<?xml version="1.0" encoding="UTF-8"?>
<gfc:FC_FeatureCatalogue
    id="_1"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:gco="http://www.isotc211.org/2005/gco"
    xmlns:gmd="http://www.isotc211.org/2005/gmd"
    xmlns:gfc="http://www.isotc211.org/2005/gfc"
    xmlns:gml="http://www.opengis.net/gml/3.2"
    xmlns:gmx="http://www.isotc211.org/2005/gmx"
    xsi:schemaLocation="http://www.isotc211.org/2005/gfc Q:\users\mize.jacqueline\ISO\gfc\gfc.xsd">

mbjones

# Example R code to filter data using criteria passed in via lists, using lapply
# Matt Jones 2015-10-19

library(dplyr)
# create a sample fake data frame
df <- cbind(expand.grid(sciName=list("A", "B", "C", "D"), family=list("X", "Y"), stage=list("S1", "S2", "S3", "S4")), count=1)
df

# set up our filter conditions
condition1 <- list(sciName="A", stageVector=c("S2", "S3"))