update catalog

catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json

@@ -11,27 +11,27 @@
     {
       "rel": "item",
       "type": "application/json",
-      "href": "./models/climatology.json"
+      "href": "./models/tg_tbats.json"
     },
     {
       "rel": "item",
       "type": "application/json",
-      "href": "./models/persistenceRW.json"
+      "href": "./models/climatology.json"
     },
     {
       "rel": "item",
       "type": "application/json",
-      "href": "./models/tg_arima.json"
+      "href": "./models/persistenceRW.json"
     },
     {
       "rel": "item",
       "type": "application/json",
-      "href": "./models/tg_ets.json"
+      "href": "./models/tg_arima.json"
     },
     {
       "rel": "item",
       "type": "application/json",
-      "href": "./models/tg_tbats.json"
+      "href": "./models/tg_ets.json"
     },
     {
       "rel": "parent",

catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json

@@ -24,7 +24,8 @@
   "properties": {
     "title": "climatology",
     "description": "All scores for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd.  Assumes normal distribution.\n                                    The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRPO, SUGG, TOMB, PRLA, TOOK.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
-    "datetime": "2024-10-23T00:00:00Z",
+    "datetime": "2024-07-03",
+    "updated": "2024-07-04",
     "start_datetime": "2024-06-13T00:00:00Z",
     "end_datetime": "2024-08-07T00:00:00Z",
     "providers": [
@@ -226,7 +227,7 @@
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
       "href": "s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3://bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology\"\ncon.read_parquet(path + \"/**\")"
     }
   }
 }

catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json

@@ -24,7 +24,8 @@
   "properties": {
     "title": "persistenceRW",
     "description": "All scores for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n                                    The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
-    "datetime": "2024-10-23T00:00:00Z",
+    "datetime": "2024-07-03",
+    "updated": "2024-07-04",
     "start_datetime": "2024-06-13T00:00:00Z",
     "end_datetime": "2024-08-06T00:00:00Z",
     "providers": [
@@ -226,7 +227,7 @@
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
       "href": "s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3://bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW\"\ncon.read_parquet(path + \"/**\")"
     }
   }
 }

catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json

@@ -24,7 +24,8 @@
   "properties": {
     "title": "tg_arima",
     "description": "All scores for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n                                    The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
-    "datetime": "2024-10-23T00:00:00Z",
+    "datetime": "2024-07-03",
+    "updated": "2024-07-04",
     "start_datetime": "2024-06-13T00:00:00Z",
     "end_datetime": "2024-08-02T00:00:00Z",
     "providers": [
@@ -226,7 +227,7 @@
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
       "href": "s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3://bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima\"\ncon.read_parquet(path + \"/**\")"
     }
   }
 }

catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json

@@ -24,7 +24,8 @@
   "properties": {
     "title": "tg_ets",
     "description": "All scores for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n                                    The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
-    "datetime": "2024-10-23T00:00:00Z",
+    "datetime": "2024-07-03",
+    "updated": "2024-07-04",
     "start_datetime": "2024-06-13T00:00:00Z",
     "end_datetime": "2024-08-02T00:00:00Z",
     "providers": [
@@ -226,7 +227,7 @@
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
       "href": "s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3://bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets\"\ncon.read_parquet(path + \"/**\")"
     }
   }
 }

catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json

@@ -9,22 +9,23 @@
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
+      [-82.0177, 29.6878],
+      [-88.1589, 31.8534],
+      [-149.6106, 68.6307],
       [-82.0084, 29.676],
       [-87.7982, 32.5415],
       [-89.4737, 46.2097],
       [-84.4374, 31.1854],
       [-89.7048, 45.9983],
       [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307]
+      [-99.2531, 47.1298]
     ]
   },
   "properties": {
     "title": "tg_tbats",
-    "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n                                    The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
-    "datetime": "2024-10-23T00:00:00Z",
+    "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n                                    The model predicts this variable at the following sites: SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO.\n                                    Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)",
+    "datetime": "2024-07-03",
+    "updated": "2024-07-04",
     "start_datetime": "2024-06-13T00:00:00Z",
     "end_datetime": "2024-08-02T00:00:00Z",
     "providers": [
@@ -55,16 +56,16 @@
       "chla",
       "Daily",
       "P1D",
+      "SUGG",
+      "TOMB",
+      "TOOK",
       "BARC",
       "BLWA",
       "CRAM",
       "FLNT",
       "LIRO",
       "PRLA",
-      "PRPO",
-      "SUGG",
-      "TOMB",
-      "TOOK"
+      "PRPO"
     ],
     "table:columns": [
       {
@@ -226,7 +227,7 @@
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
       "href": "s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `R` or `Python` code for remote access to the database. This code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n\n       con = ibis.duckdbf.connect()\n\n\n       con.raw_sql(f'''\n\n       CREATE OR REPLACE SECRET secret (\n\n       TYPE S3,\n\n       ENDPOINT 'sdsc.osn.xsede.org',\n\n       URL_STYLE 'path'\n\n\n       );/n\n       '''\n\n\n       path = \"s3://bio230014-bucket01/challenges/scores/bundled-parquet//project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats\"\ncon.read_parquet(path + \"/**\")"
     }
   }
 }