Overview: The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose: This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data: 1. Standards for metadata and data formats 2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security) 3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants) 4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed) 5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive) 6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

Surface under the cumulative ranking curve (SUCRA) results of the outcomes.

This reference archives tabular data collected for Moth (Lepidoptera) Inventory survey (PRIMR Survey ID: TBD). This database documents moths trapped on William L. Finley NWR during surveys performed in 2020.

A full dump of OCTOPUS PostgreSQL database v.2.1 as published upon

a sematic database redesign (effective db v.2),

the creation of a fully relational PostgreSQL database that uses the PostGIS spatial extension (effective db v.2),

moving the database to GCP (effective db v.2),

fostered FAIR, OPEN and CARE principles implementation (effective db v.2),

the introduction of 'SahulSed' replacing 'OSL/TL Australia' (effective v.2(1)),

the integration of the 'FosSahul' partner collection (effective v.2(1)),

the integration of the 'ExpAge' partner collection (effective v.2(1)),

major upgrades to the 'CRN INT' and 'CRN AUS' collections (effective v.2(2)),

the integration of the 'SahulArch' collection (v.2.1(2)).

Accompanying publication: Codilean, A. T., Munack, H., Saktura, W. M., Cohen, T. J., Jacobs, Z., Ulm, S., Hesse, P. P., Heyman, J., Peters, K. J., Williams, A. N., Saktura, R. B. K., Rui, X., Chishiro-Dennelly, K., and Panta, A.: OCTOPUS database (v.2), Earth Syst. Sci. Data, 14, 3695–3713, https://doi.org/10.5194/essd-14-3695-2022, 2022.

Group average map of white matter volume images in standard MNI space across 1,832 MRiShare subjects.

Collection description

This collection contains group average maps presented in the associated publication "The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students".

Subject species

homo sapiens

Modality

Structural MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

A

Risk ratio/mean difference (95% CI) of the FEV1% and FEV1/FVC%.

William Wallace, LLC Whois Database, discover comprehensive ownership details, registration dates, and more for William Wallace, LLC with Whois Data Center.

William the Conqueror, LLC Whois Database, discover comprehensive ownership details, registration dates, and more for William the Conqueror, LLC with Whois Data Center.

Components of CMIs.

WILLIAM Meteo Database

Introduction of project WILLIAM:

Wide-field imaging is a popular way of monitoring the night sky to get a real-time view of current weather conditions. The project WILLIAM (Wide-field all-sky image analyzing monitoring system) was created on demand to provide autonomous control of the telescope and observatory dome. The main goal of this project was to develop a low-cost wide-field and high resolution camera system, whose image data is can be archived for later analysis. One of the options of evaluating current weather conditions from the captured image data is to count visible stellar objects. To work properly, the system must be calibrated to a minimum number of visible stellar objects. If actual image data includes less detected stellar objects than it is calibrated for, the system evaluates the possible occurrence of clouds or rain. Such conditions are then interpreted as inappropriate for using a telescope. Thus the observatory dome stays closed or is going to be closed. The detection of clouds can also be carried out directly in the vicinity to mid-IR. The advantage of IR-based systems is the possibility to detect clouds under any conditions. However, these systems require very complicated and expensive optics and detectors.

Dataset

Original and support test image data for the research letter "Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images".

Data for this database are provided from the WILLIAM system located in Jarošov nad Nežárkou (South Bohemia, GPS 49.185N, 15.072E).

Images are stored in the original raw NIKON format NEF in the separate WMD_NEF.zip file. Support images with clustered data in LAB color space and XYZ color space are located in the LAB_clusters.zip and XYZ_clusters.zip files. Cloud annotation (attributes) for LAB and XYZ clustering is present within the WMD.xlsx file.

attributes

Image Number

ID (name of image file)

Day image number (number of image in a day of capturing)

Date (date of image capturing)

Time (time of capturing)

Cluster with sun (index of the cluster from the support cluster image files that includes the sun - only if the sun was present)

Clear sky (index of one cluster or several clusters from the support cluster image files that include clear sky part of image)

Cumulus (index of one cluster or several clusters from the support cluster image files that include cumulus cloud part of image)

Stratus (index of one cluster or several clusters from the support cluster image files that include stratus cloud part of image)

Stratocumulus (index of one cluster or several clusters from the support cluster image files that stratocumulus cloud part of image)

Nimbostratus (index of one cluster or several clusters from the support cluster image files that include nimbostratus cloud part of image)

Altocumulus (index of one cluster or several clusters from the support cluster image files that include altocumulus cloud part of image)

Altostratus (index of one cluster or several clusters from the support cluster image files that include altostratus cloud part of image)

Cumulonimbus (index of one cluster or several clusters from the support cluster image files that include cumulonimbus cloud part of image)

cirrocumulus (index of one cluster or several clusters from the support cluster image files that include Cirrocumulus cloud part of image)

Cirrostratus (index of one cluster or several clusters from the support cluster image files that include cirrostratus cloud part of image)

Cirrus (index of one cluster or several clusters from the support cluster image files that include cirrus cloud part of image)

Edges (index of the cluster that includes masked edges of the image)

Rain (values 0 or 1 if the rain was present)

Cloud groups (main cloud classification groups)

1. high-level clouds (index of one cluster or several clusters from the support cluster image files that include high-level clouds in the image)

2. low-level (cumulus type) clouds (index of one cluster or several clusters from the support cluster image files that include low-level clouds in the image)

3. rain clouds (index of one cluster or several clusters from the support cluster image files that include rainy clouds in the image)

4. clear sky (index of one cluster or several clusters from the support cluster image files that include clear sky in the image)

Time distance from solar noon (in hours)

Time distance from Sunset or Sunrise (in hours)

Sun elevation (in degrees)

Note: The classification of the exact cloud class within the all-sky image is mainly tentative. The cloud group division served for classification purposes.

The WMD.xlsx file consists of two separate clustering annotations in LAB and XYZ colour spaces. The file also includes the EXIF data infromation of each image.

Risk ratios/Mean difference (95%CIs) of the TLC and DLCO.

Risk ratio/mean difference (95% CI) of the TGF and IIIC.

Risk ratio/mean difference (95% CI) of the CER and PaO2.

The AOM for WM segmentation of the simulated databases with 3% and 9% noise and 0% and 40% bias field.

The Global Species Databases hosted within WoRMS. Those with their own web entry page are underlined.

Risk ratios/Mean difference (95%CIs) of the PaCO2 and FVC.

Demographic characteristics of western-medicine-only users (WM-only users), traditional-medicine-only users (TM-only users) and those both use WM and TM (WM &TM users) in South Korea and Taiwan in 2011.

The (a) Regional Species Databases (RSD) and (b) Thematic Species Databases (TSD), hosted within WoRMS, and their editors.

This database contains all studies in which at least one relevant outcome measure was investigated for both OB and OC. Characteristics of cells, methods and culture conditions, and descriptive statistics are listed in this database. (XLSM)

The countries and institutes represented by the editors of WoRMS and its associated databases. These are mapped at http://www.marinespecies.org/imis.php?module=gmap&spcolid=507.