RDS is an API that can be used by a variety of users through the lifecycle of a data product. Data publishers and managers are able to document, expose, and disseminate their data. Researchers can navigate large datasets, quickly locate, and retrieve the data they need. Developers can quickly build applications, dashboards, and stories through the quick and convenient API. Best of all, the documentation is returned through the API as well, ensuring that no one is left in the dark about the data they are working with. The following are a few stories demonstrating how RDS can help each role in their day-to-day activities.
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In addition to the records of data the variable metadata will be returned for any of the variables included in the query. The index of the variables should match the index of the values in the returned records.
Not to be confused with including metadata, injecting metadata indicates that metadata will be injected into the data records that are returned. For example, if a variable has a classification associated with it the code labels (or the entire code depending on the format) will be returned in the record instead of the raw data value. Metadata can be injected through the use of the inject parameter.
This section will explore some of the basic query capabilities that can be performed in RDS. This will included running a basic query, retrieving data and metadata together, paging through a data product, sorting, and filtering.
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\nIn this example we will query for the state, number of persons in the household, the number of persons greater than 60 years old, the number of people greater than 65 years old, and the number of persons under 18. Notice that the state and persons in the household are queried directly, the other 3 variables are queried using a regular expression p[0-9]+ (encoded in the URL).
In a relational database if users want to exclude one column they need to write a query to select all the columns except that one. RDS allows users to specify columns they want to exclude by prefixing the column name with a ~.
We will query every variable except the SERIALNO, which is the first variable. With the serial number excluded the state should be the first variable in the returned columns.
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\"isPrivate\": false,\n \"label\": \"Sex\",\n \"name\": \"SEX\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"39f2a156-50ff-4b7d-9101-e1a2eff5a1fb\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"SEXA\",\n \"isPrivate\": false,\n \"label\": \"Sex Allocation Flag\",\n \"name\": \"SEXA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0216,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"d52940e0-37ba-4784-974b-f34d349118f1\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"SSPA\",\n \"isPrivate\": false,\n \"label\": \"Same Sex Spouse Flag\",\n \"name\": \"SSPA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0011,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"55351726-6ef8-4bb6-9c73-09e4f336c32f\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 2,\n \"id\": \"AGE\",\n \"isPrivate\": false,\n \"label\": \"Age\",\n \"name\": \"AGE\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 91,\n \"distinctSet\": [],\n \"max\": 93,\n \"mean\": 37.3733,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"b47da474-683e-493c-a1c8-db9355ca82dc\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"AGEA\",\n \"isPrivate\": false,\n \"label\": \"Age Allocation Flag\",\n \"name\": \"AGEA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0528,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"dbcbc52a-cf9b-40dc-b4fc-f9aae2516e57\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"QTRBIR\",\n \"isPrivate\": false,\n \"label\": \"Quarter of Birth\",\n \"name\": \"QTRBIR\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 4,\n \"distinctSet\": [],\n \"max\": 3,\n \"mean\": 1.5129,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"1ead5d20-b84f-4e9a-8382-19df7c65e3b5\"\n },\n {\n \"classificationUri\": \"bb1beec6-7f33-4091-90c3-1da6c6fd0ca7\",\n \"dataType\": \"TEXT\",\n \"fixedStorageWidth\": 2,\n \"frequencies\": {\n \"sets\": [\n {\n \"map\": {\n \"11\": 26851,\n \"25\": 10647,\n \"01\": 1405017,\n \"02\": 27905,\n \"03\": 14270,\n \"Other (group of all values that accounted for less than 0.5%)\": 43232\n },\n \"weighted\": false,\n \"weights\": []\n }\n ]\n },\n \"id\": \"HISPAN\",\n \"isPrivate\": false,\n \"label\": \"Hispanic or Latino Origin\",\n \"name\": \"HISPAN\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 25,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"b75869c9-ad41-4079-9d70-24624d61b9ab\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"HISPANA\",\n \"isPrivate\": false,\n \"label\": \"Hispanic or Latino Origin Allocation Flag\",\n \"name\": \"HISPANA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0509,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"3016e1b3-8263-4b0e-95f5-32282fb7b04a\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"NUMRACE\",\n \"isPrivate\": false,\n \"label\": \"Number of Major Race Groups Marked\",\n \"name\": \"NUMRACE\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 6,\n \"distinctSet\": [],\n \"max\": 6,\n \"mean\": 1.0313,\n \"min\": 1,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"1cf8f8c9-5bbe-4faf-8b7b-f8c9f69806fc\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"WHITE\",\n \"isPrivate\": false,\n \"label\": \"White recode\",\n \"name\": \"WHITE\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.6578,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"f081a319-00bf-4299-9dd1-37ac74b67c77\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"BLACK\",\n \"isPrivate\": false,\n \"label\": \"Black or African American recode\",\n \"name\": \"BLACK\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.2594,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"7ccf7e85-722e-430f-8dfc-69e24ecc3c23\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"AIAN\",\n \"isPrivate\": false,\n \"label\": \"American Indian and Alaska Native recode\",\n \"name\": \"AIAN\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0102,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"ea8553e3-3ee2-48a2-8a9f-acfd2a51210f\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"ASIAN\",\n \"isPrivate\": false,\n \"label\": \"Asian recode\",\n \"name\": \"ASIAN\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0624,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"26b307ef-04d1-43b4-b2c0-81e2016dc8e7\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"NHAW\",\n \"isPrivate\": false,\n \"label\": \"Native Hawaiian recode\",\n \"name\": \"NHAW\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0005,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"e325833b-6e89-40eb-9a88-871f8f3136d0\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"OPI\",\n \"isPrivate\": false,\n \"label\": \"Other Pacific Islander recode\",\n \"name\": \"OPI\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0014,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"171cd4b4-208e-40c0-bd59-539000c90377\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"OTHER\",\n \"isPrivate\": false,\n \"label\": \"Some other race recode\",\n \"name\": \"OTHER\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0397,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"6363120b-5716-4ba7-b5e4-d7a7ee315ce1\"\n },\n {\n \"classificationUri\": \"931ee7c4-4ef5-4ba2-8b4a-6ded8543d91c\",\n \"dataType\": \"TEXT\",\n \"fixedStorageWidth\": 2,\n \"frequencies\": {\n \"sets\": [\n {\n \"map\": {\n \"10\": 51420,\n \"11\": 43659,\n \"01\": 969761,\n \"02\": 375570,\n \"06\": 80854,\n \"Other (group of all values that accounted for less than 0.5%)\": 6658\n },\n \"weighted\": false,\n \"weights\": []\n }\n ]\n },\n \"id\": \"RACESHORT\",\n \"isPrivate\": false,\n \"label\": \"Race Short Recode\",\n \"name\": \"RACESHORT\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 11,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"34ac17d0-974d-4883-8a6b-6ec6a0b52007\"\n },\n {\n \"classificationUri\": \"8515e151-a020-47a2-86b3-a21a9bc308c6\",\n \"dataType\": \"TEXT\",\n \"fixedStorageWidth\": 2,\n \"frequencies\": {\n \"sets\": [\n {\n \"map\": {\n \"40\": 20016,\n \"45\": 13007,\n \"47\": 11654,\n \"51\": 12426,\n \"59\": 8087,\n \"73\": 51420,\n \"74\": 43659,\n \"01\": 969761,\n \"02\": 375570,\n \"Other (group of all values that accounted for less than 0.5%)\": 22322\n },\n \"weighted\": false,\n \"weights\": []\n }\n ]\n },\n \"id\": \"RACEDET\",\n \"isPrivate\": false,\n \"label\": \"Race Detailed Recode\",\n \"name\": \"RACEDET\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 72,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"9d766dcc-df00-49fc-8f19-c0f4a4736a2a\"\n },\n {\n \"classificationUri\": \"8791f4d1-97c9-4849-bded-b390bfadeb1f\",\n \"dataType\": \"TEXT\",\n \"fixedStorageWidth\": 3,\n \"frequencies\": {\n \"sets\": [\n {\n \"map\": {\n \"001\": 969761,\n \"002\": 375570,\n \"015\": 51420,\n \"004\": 20016,\n \"005\": 13946,\n \"008\": 12426,\n \"016\": 11884,\n \"006\": 11654,\n \"010\": 11532,\n \"009\": 8087,\n \"Other (group of all values that accounted for less than 0.5%)\": 41626\n },\n \"weighted\": false,\n \"weights\": []\n }\n ]\n },\n \"id\": \"RACECHKBX\",\n \"isPrivate\": false,\n \"label\": \"Race Checkbox Recode\",\n \"name\": \"RACECHKBX\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 108,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"0b3c101e-9c05-4b45-85b4-f22edebc559f\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"decimals\": 0,\n \"fixedStorageWidth\": 1,\n \"id\": \"RACEA\",\n \"isPrivate\": false,\n \"label\": \"Race Allocation Flag\",\n \"name\": \"RACEA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.042,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"987484d6-3971-4ab4-b48f-b021cc1b524d\"\n },\n {\n \"classificationUri\": \"f00a1f04-330e-4bee-9d3e-1283acfb6e2b\",\n \"dataType\": \"TEXT\",\n \"fixedStorageWidth\": 1,\n \"frequencies\": {\n \"sets\": [\n {\n \"map\": {\n \"0\": 1483657,\n \"1\": 18711,\n \"2\": 25554\n },\n \"weighted\": false,\n \"weights\": []\n }\n ]\n },\n \"id\": \"GQTYP\",\n \"isPrivate\": false,\n \"label\": \"Group Quarters Type\",\n \"name\": \"GQTYP\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"VARCHAR\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 3,\n \"distinctSet\": [],\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"5995cb0d-648d-4774-8452-db421f5cdb99\"\n },\n {\n \"dataType\": \"NUMERIC\",\n \"fixedStorageWidth\": 1,\n \"id\": \"GQTYPA\",\n \"isPrivate\": false,\n \"label\": \"Group Quarters Allocation Flag\",\n \"name\": \"GQTYPA\",\n \"reference\": false,\n \"revisionNumber\": 0,\n \"storageType\": \"INT\",\n \"summaryStatistics\": {\n \"sets\": [\n {\n \"distinct\": 2,\n \"distinctSet\": [],\n \"max\": 1,\n \"mean\": 0.0001,\n \"min\": 0,\n \"missing\": 0,\n \"populated\": 1527922,\n \"weighted\": false\n }\n ]\n },\n \"uri\": \"263ea183-943d-4638-b037-b755b38e43f1\"\n }\n ]\n}"}],"_postman_id":"81ef0251-6f7f-4de1-a192-f0d95d16d528"},{"name":"Query by Keyword","event":[{"listen":"test","script":{"id":"7f450de4-9f98-4d88-9c5f-e0ff59b1cfda","exec":["","pm.test(\"Status code is 200\", function () {"," pm.response.to.have.status(200);","});",""],"type":"text/javascript"}}],"id":"324ee242-8d60-4b13-9ca7-bc82261ebc36","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"Content-Type","value":"application/json"}],"url":"http://{{host}}/rds/api/query/uscensus2010/hp-mysql/select?cols=$age,$sex,$race&metadata&limit=1","description":"When there is more robust variable metadata published in RDS, keywords can be used to query for columns. This will search all the variable metadata for the specified keywords and return any columns that contain them. The keywords can be specified by prefixing the keyword with $.
Users querying the census data may be interested in the demographic data that the data product contains. To search for these variables, keywords can be used to get the appropriate variables regardless of if the exact variable name is known. In this example we query for $age, $sex, and $race, and 13 variables are returned.
Recoded variables can computed and queried for along side of any other variables in the data product. To recode a variable the following syntax can be applied newVariableName:originalVariable[*:1='Under 1';2='2';3:*='Over 3']. In this case a new variable is created by grouping values from the original variable into 3 sets. Anything under 1 is grouped, 2 is returned in text format, and 3 and above is grouped. Any values that are left out of the expression will be returned with the orginal value.
Users are interested in creating age groups from the data rather than having a distinct age per response. In this example we will query for age as normal, but also create a recoded bandedAge variable and return them side by side. The limit=1 has been removed to allow for a more compelling example. The bandedAge column is created with the following syntax [*:18='Under 18';19-35='19 to 35';36-70='36 to 70';70:*='Over 79'] (URL encoded in the example).
The select service is a part of the RDS API that give access to record level data. This service can be good to browse the data of the data product and discover what the data is actually like. It could be used to create interactive tables where data and documentation can be seen along side each other.
If desired, the variable metadata can be returned with the query by including the metadata parameter. In addition to the records of data the variable metadata will be returned for any of the variables included in the query. The index of the variables should match the index of the values in the returned records.
Not to be confused with including metadata, injecting metadata indicates that metadata will be injected into the data records that are returned. For example, if a variable has a classification associated with it the code labels (or the entire code depending on the format) will be returned in the record instead of the raw data value. Metadata can be injected through the use of the inject parameter.
While data can be queried immediately after setting up a data product, managers can continue to enhance the data product through the process of documenting the data (creating metadata). This could be updating variable names to be more meaningful, adding labels, descriptions, classifications, or one of many other properties on the variables in the data product. The addition of metadata helps bring meaning and intention to the data. This is vital for robust user interfaces, processing, and even the generated data packages that RDS performs. In addition, metadata allows users to discover the data they are looking for easily.
\nFor data products that have more than a handful of variables it is rare for users to actually want to view an entire data set. Most of the time it is expected for users to subset their data based on variables or topics of interest. This brings up a couple interesting issues for users trying to access the data. If users are unfamiliar with the data source or variable names, how are they supposed to select the variables they are interested in? Are users expected to search through hundreds or thousands of variables in order to find the variable they are interested in? To solve this problem, RDS leverages the documentation that administrators have added to provide enhanced selection capability.
\nThe following examples of Rich Queries are performed on the 2010 US Census PUMS (household / person) data. This is a 10% sample of the DC metropolitan area (DC, DE, MD, and VA). It has 56 variables and 1.5 million records.
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\n","urlObject":{"protocol":"http","path":["rds","api","catalog","{{catalogId}}","{{dataProductId}}","variables\n"],"host":["{{host}{"],"query":[],"variable":[]}},"response":[],"_postman_id":"d7e4972a-73da-40de-9e5f-cc1649a92fbb"},{"name":"Getting More Information About a Variable","id":"b34ca21f-fe0e-4561-9cc8-2303af0278c8","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[],"url":"{{baseUrl}}/rds/api/catalog/{{catalogId}}/{{dataProductId}}/variable/{{variableId}}","description":"The variable summaries are a good place to start when presenting the metadata to users, but to save querying time they are not used to store all of the variable information (summary statistics and frequencies for instance). If a user selects a variable, it would make sense to get a full variable with all the information available.
\n","urlObject":{"path":["rds","api","catalog","{{catalogId}}","{{dataProductId}}","variable","{{variableId}}"],"host":["{{baseUrl}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"b34ca21f-fe0e-4561-9cc8-2303af0278c8"},{"name":"Getting a Classification","id":"e79e2e59-a0d7-42cd-96d1-4240fb1c4d35","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[],"url":"{{baseUrl}}/rds/api/catalog/{{catalogId}}/{{dataProductId}}/classification/{{classificationUri}}","description":"Classifications are essentially a set of codes and their values, and are often vital to understanding the data stored in any given data product. If a variable has a classification associated with it it will have the 'classification URI' property filled out. This can be used to retrieve high level information about the classification. Beware though, due to the fact that some classifications can be extremely large (100ks of codes) no codes will be returned on the classification, they need to be queried by themselves.
\n","urlObject":{"path":["rds","api","catalog","{{catalogId}}","{{dataProductId}}","classification","{{classificationUri}}"],"host":["{{baseUrl}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"e79e2e59-a0d7-42cd-96d1-4240fb1c4d35"},{"name":"Building a Classification","id":"9370108a-fdfe-4a2b-963e-b784d043e719","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[],"url":"{{baseUrl}}/rds/api/catalog/{{catalogId}}/{{dataProductId}}/classification/{{classificationUri}}/codes","description":"Due to the fact that classifications can be very large, separate calls must be made to get a subset of codes in batches. These can be used to build out a classification as a user browsers / paginates through the codelist.
\n","urlObject":{"path":["rds","api","catalog","{{catalogId}}","{{dataProductId}}","classification","{{classificationUri}}","codes"],"host":["{{baseUrl}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"9370108a-fdfe-4a2b-963e-b784d043e719"}],"id":"deee19e5-cab3-4004-8d17-c27c04c9b574","_postman_id":"deee19e5-cab3-4004-8d17-c27c04c9b574","description":""},{"name":"Customized Data Packaging","item":[],"id":"caf275db-705d-4d87-8d1d-8fc3740713e0","_postman_id":"caf275db-705d-4d87-8d1d-8fc3740713e0","description":""}],"event":[{"listen":"prerequest","script":{"id":"5f5b4f6e-e425-47e9-8a73-b9ff239b1868","type":"text/javascript","exec":[""]}},{"listen":"test","script":{"id":"2d23e5ef-b9dd-4033-af09-37d2e8217258","type":"text/javascript","exec":[""]}}]}