DRAKO is a Mobile Location Data provider with a programmatic trading desk specializing in geolocation analytics and programmatic advertising. Our Consumer Travel History Data has helped cities, counties, and states better understand who their visitors are so that they can effectively develop and deliver advertising campaigns. We’re in a unique position to deliver enriched insight beyond traditional surveying or other data sources because of our rich dataset, proprietary modelling capabilities, and analytical capabilities.

MAIDs (Mobile Advertising IDs) are unique device identifiers associated with consenting mobile devices that can be utilized for geolocation based analyses and audiences. Drako uses MAIDs to fuel our Consumer Travel History Data utilizing our Home Location Model. The Home Location of a MAID is determined based on where that MAID is seen most frequently between the hours of 11pm and 6am (local time). Using this we are able to determine the Home Location of a user which in turn allows us to identify when and where they are travelling.

Beyond identifying that users are tourists, we can also classify them into different bins by their frequency / dwell time over their estimated number of visits. Using our data and frequency, we can identify: overnight visitors, weekend visits, short-term stays, long-term stays, or frequent holiday visitors !

Beyond Consumer Travel History Data in your defined geography alone, we are also able to provide: - Home location - Find out where your audience is coming from using our home location technology - Movement - Quantify how far users have travelled between locations. - Demographics - Discover neighborhood level characteristics such as income, ethnicity, and more - Brand index - Learn which major brands and retailers your audience is visiting the most. - Visitation index - See which destinations your visitors are visiting the most - Addressable audience - Customize your audiences for your campaigns using our analytic insights

Moreover, if you’re looking to activate your Consumer Travel History Data for advertising, we’re always able to further refine or filter your desired audience with our other Audience Data, such as: Brand visits, Geodemographics, Ticketed Event visits, Purchase Intent (in Canada), Purchase History (in USA), and more !

Data Compliance: All of our Consumer Travel History Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

Dataset

This dataset was created by Taylor F

Contents

Open Access Data. Hitt, NP, Landsman, AP, and Raesly, RL. 2022. Life history strategies of stream fishes linked to predictors of hydrologic stability. Ecology and Evolution 12.e8861.

MODFLOW6 and SNTEMP models were developed to simulate groundwater flows and instream temperatures in Beaver Creek, Alaska from 2019-2023 using python scripts to create a reproducible workflow to process input datasets into model files. This data release contains the scripts used to build the SNTEMP and MODFLOW models, process model output to compare to field observations, and develop and run the PEST++ workflow for history matching. These workflows are described in the readme.md files in this archive and are used to implement the modeling decisions described in the associated report, "Simulating present and future Groundwater/Surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska".

Collection

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

The Geofabric Surface Network product provides a set of related feature classes to be used as the basis for production of consistent hydrological surface stream network analysis. This product contains a topographically consistent representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs and other hydrographic features).

The Geofabric Surface Network product is based upon the input from ANUDEM Derived Streams V1.1.2 (ANUDEM Streams) which is the vectorised version of the nine second ANUDEM derived raster steams product. The product is related to, but distinct from, the stream network contained in the Geofabric Surface Cartography product. The network product represents the flow direction of streams over the surface of the terrain, based on the GEODATA Nine Second Digital Elevation Model (DEM-9S) Version 3. This product is more generalised than the Geofabric Surface Cartography and represents the main channels of the stream, particularly in areas where streams are heavily anabranched or disconnected.

In addition, the stream connectivity represents a stream flow over the terrain, regardless of the presence of a corresponding Geofabric Surface Cartography stream segment. This means that the Geofabric Surface Cartography product may represent a stream as an interrupted or intermittent feature, whereas this product represents the same stream as a continuous connected feature. That is, the path that a stream would take (according to the terrain model) if sufficient water were available for flow. This product is fully topologically correct which means that all the stream segments flow in the correct direction. It also has full connectivity based on the flow of water across a terrain model.

This product contains six feature types including: Waterbody, Network Stream, Network Node, Catchment, Network Connectivity (Upstream) and Network Connectivity (Downstream).

Purpose

This product contains a topographic representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for production of consistent surface stream network analysis.

Geofabric Surface Network is intended to be used in stream flow tracing operations, using its full topological connection. The product can support the spatial selection of associated hydrological features as inputs for spatial analysis/modelling.

This product is intended to supplement the Geofabric Surface Cartography, Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments data products. This product is also used to support the definition of the Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments products and provides a spatial framework for analysis and assessment of streams and their catchments.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

Lineage statement: Geofabric Surface Network is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The geometry of this product is largely derived from the ANUDEM Derived Streams V1.1.2 (ANUDEM Streams). It consists of water bodies such as swamps, reservoirs, lakes, etc as derived from AusHydro V1, as well as the stream lines and stream line connectors through these water bodies. The ANUDEM Streams are firstly vectorised to be usable in vector line feature format and are then informed and modified by the coincident locations of the AHGFMappedStream feature class. The features are organised into specific feature class subtypes, based upon both the inputs from the AusHydro V1.7.2 and their behaviour within the AHGF Network Stream relationships. All of the AHGFNetworkStream and AHGFWaterbody features participate in the connected stream flow topology.

This product also contains the AHGFCatchment features that are derived from the National Catchment Boundaries V1.1.4. The AGHFCatchment feature class consist of the lowest level stream flow catchments based upon the inputs from ANUDEM Streams. The catchment boundaries are based upon a single AHGFNetworkStream extent over GEODATA National 9 Second DEM grid. These catchments form the basis of aggregated catchment boundaries, either by Contracted Nodes or by Pfafstetter ID Levels.

All of these features participate in the connected stream flow topology.

Changes at v2.1

! Addition of Beta Monitoring Point Table including 479 ghost nodes

 connected to the network.

- New Water Storages in the WaterBody FC.

Changes at v2.1.1

! Replacement of Beta Monitoring Point Table and inclusion of 3,310

(formerly 479) ghost nodes connected to the stream network.



! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- SegnoLink attribute update to fix single catchment feature in Tasmania.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



! Metadata updated adding explanation of AHGFNetworkStream AusHydroEr codes

and revision made to description of DrainID field.



- Fixed a series of NoFlow catchments (small internally draining catchments

not related to a stream segment) in Murray-Darling were incorrectly

attributed as externally draining via the ExtrnlBasn field in

AHGFCatchments.



! Usage of the MergedSink attribute changed from v2.1 (see

HR_Catchments_Technical_Overview.pdf for more info).

Processing steps:

1. ANUDEM Streams dataset is received and loaded into the Geofabric development GIS environment.

2. Feature classes from ANUDEM Streams are recomposed into composited Geofabric Feature Dataset Feature Classes in the Geofabric Maintenance Geodatabase.

3. Re-composited feature classes in the Geofabric Maintenance Geodatabase Feature Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84).

4. Feature classes from the Geofabric Maintenance Geodatabase Feature Dataset are extracted and reassigned to the Geofabric Surface Network Feature Dataset within the Geofabric Surface Network Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Network - V2.1.1. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d84e51f0-c1c1-4cf9-a23c-591f66be0d40.

Aim Contemporary dispersal constraints and environmental conditions are broadly recognized as significant drivers of beta diversity patterns. However, beta diversity patterns may also reflect the legacy of past climatic and geological events. In this study, we investigated the relative importance of historical and contemporary factors as drivers of taxonomic, functional and phylogenetic beta diversity in Neotropical stream communities.

Location The Colombian Orinoco basin.

Major taxa studied Diatoms and insects.

Methods We estimated taxonomic, phylogenetic, and functional beta diversity using Baselga’s (BAS) and Podani’s (POD) frameworks. Following both frameworks, we further partitioned every biodiversity facet into turnover and nestedness or richness difference components. Then, we used generalized linear models (GLM) to relate each biodiversity facet with environmental, spatial and historical factors.

Results We found that both historical and contemporary factors affected current patterns of beta diversity. Historical factors and water pH and temperature had the strongest effect on beta diversity patterns, particularly for taxonomic and phylogenetic facets. GLM models performed better for insects than for diatoms in all three facets. Within communities, our analysis also revealed a partial congruence between BAS- and POD-based results.

Main conclusions Due to their past geological history and contemporary environmental gradients, tropical montane streams are natural laboratory for disentangling the joint effects of ecological and biogeographical factors on biodiversity patterns. Our study reveals that present-day distribution patterns cannot be fully explained without accounting for the effects of past geological and climatic events on mountain landscapes. In the Neotropics, montane geology sets the stage for speciation and landscape formation, with which ecological (e.g., dispersal limitation) and environmental factors interact to generate spatial variation in species turnover.

Methods Study area

The Orinoco basin is the third largest basin in South America, covering an area of about 990,000 km2that is in most of Venezuela and in the eastern part of Colombia(Romero Ruz, Galindo Garca, Otero Garca, Armenteras Pascual, 2004). The complex geological and climatic history of the basin has shaped a broad range of ecosystems across heterogeneous landscapes(Romero Ruz et al., 2004).

In total, we sampled 26 (for diatoms) and 32 (for insects) stream segments during the dry season of 2017(January-February). In each stream segment (100 to 200m long), we selected three riffle areas that were representative of the range of substratum types, flow velocities, channel widths and depths, and canopy cover occurring along the stream. Physical and chemical variables were measured during invertebrate sampling (January-February 2017) and on two further occasions (November 2016 and, January-February 2018), that corresponded to high and low water flows, respectively. Instantaneous discharge was estimated in the three riffles by measuring of water depth and flow velocity at 15cm intervals along three cross-sections. At each interval, we also recorded the dominant substrate. Flow velocitywas measured with a digital flow meter (SCHILTKNECHT MiniAir 20). Canopy shading (%) was estimated from vertical photographs using a fisheye lens and subsequent image analysis.Conductivity, pH, oxygen, and temperature were recorded using a HANNA HI98194 water quality meter upon arrival (early morning) and departure (dawn) from the site.

On each occasion, 1 L of water was collected for physico-chemical analyses, filtered through 0.7m glass fiber filters (Whatman GF/F, Kent, UK) and stored frozen until analysis. In the laboratory, ammonium and nitrate concentrations were determined on a Dionex ICS-5000 ion chromatography system (Dionex Corporation, Sunnyvale, U.S.A.). Reactive phosphorus (PRS) concentrations were determined colorimetrically using the fully automated discrete analyzer Smartchem 140 (AMS Allaince, Frpillon, France). Total suspended solids (TSS) were analyzed by filtering 500ml of water through a pre-weighed GFF and drying the filtrate for 1 hour at 105C. The mean and coefficient of variation of all the variables per ecoregion are summarized in Table S1.

Longer-term hydrological variables were estimated using the rational method modified byTmez (2003). This method estimates a streams water flow as function of the total precipitation, the basin area and associated land uses, the time of concentration, and the runoff coefficient (Supplementary Material).Once the daily water flow had been determined, we estimated the threshold at which the streams basal flow was surpassed, as a unit of disturbance for the invertebrate communities.We then calculated: (i) the number of days elapsed since the last flood event (defined as the one doubling the basal flow discharge); (ii) the number of flood events; and (iii) the ratio between the maximum and basal flow discharges.

Invertebrate sampling

Insects were collected using a multi-habitat sampling procedure, with 5 Surber (mesh size: 350mm; area: 0.09 m2) samples collected in stream substrata that were selected according to their corresponding habitat coverage. For instance, if a riffle was composed of 60% of boulders, 30% gravel, and 10% cobbles, 3 Surber samples of the first, 1 of the second, and 1 of the third substratum type were collected.The substratum distribution in each riffle was evaluated visually using the Wentworth scale (mm, diameter-based) as a reference(Wentworth, 1922).We only sampled boulders (diameter 250mm) smaller than the sample frame. In six of the 32 streams sampled, only two riffle sections (10 Surber samples) were sampled because of problems with access.

In the laboratory, invertebrates were sorted and identified to the genus level, followingTrivinho-Strixino Strixino (1995), Merritt Cummins (2008), Domnguez Fernndez (2009) and Gonzlez-Crdoba et al.(2015). Chironomidae and Ephemeroptera were dissected and mounted in Euparal following the protocol ofDomnguez (2006) and Andersen et al. (2013). The pupae of Chironomidae were mounted to confirm some taxonomical identities (Prat et al. 2014).

Diatom sampling

We sampled diatom communities at three different riffle sections of each stream segment, each section spanning from 20 to 60m long.At each riffle section, we collected 8 cm2of surface, brush-scraped algal material from 30 boulders and cobbles. In the case of streams from Guiana shield and high-Plains, where boulders and cobbles were scarce, we also took samples from bedrock, pebbles and sand. Algal material was pooled by riffle section (= 3 samples per stream segment) and subsequently preserved in aTranseausolution. In the laboratory, the organic material from samples was cleaned using hydrogen peroxide. Clean diatom frustules were mounted on permanent slides using a Naphrax medium, the slides were then observed under a 1000x light microscope and identified at the finest level possible using specialized monographs(Krammer and Lange-Bertalot 1986, 1991, Metzeltin and Lange-Bertalot 2007, Bellinger and Sigee 2015). At least 400 valves were counted in each slide.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the Bureau's Hydstra Systems dataset. The source datasets is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset provides a shapefile of stream gauging stations that include both active and past stream gauges in the Cooper Creek and Diamantina river basins of the Lake Eyre Basin Bioregion. Source data was provided by the Bureau's Hydstra Systems. Majority of these gauges record stage height and discharge. Only a few gauges record water quality parameters (e.g. salinity, turbidity, water temperature) in addition to stage height and discharge.

Purpose

Discharge data were used to estimate mean annual flow and overall flow regime.

Dataset History

This dataset uses locations of gauging stations for which stream flow time-series data were extracted from the Bureau's Hydstra system.

Dataset Citation

Bioregional Assessment Programme (2015) Cooper Creek and Diamantina River stream gauges. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/54165adb-7d51-4e39-b919-571ddc800362.

Dataset Ancestors

• Derived From National Surface Water sites Hydstra

Explore the historical Whois records related to streaming-data.info (Domain). Get insights into ownership history and changes over time.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition.

The Department of Environment, Land, Water and Planning (DELWP) monitors and reports on the health and availability of Victoria's water resources through a number of programs and partnerships. The Water Measurement Information System is the primary access point to search, discover, access and download surface water and groundwater monitoring data collected by DELWP and its partners.

Purpose

Further information about water quality and water quantity can be found at http://data.water.vic.gov.au/monitoring.htm

Dataset History

Collecting information about groundwater has been occurring for many decades. Numerous resources have been written about the general process. No specific documents have been prepared to describe the history, processes and methods of water measurement in Victoria.

Further information about water quality and water quantity can be found at http://data.water.vic.gov.au/monitoring.htm

Dataset Citation

Victorian Department of Environment, Land, Water and Planning (2015) Stream gauge locations - Victorian Water Management Information System. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/ba088d7a-1972-48b2-a4e1-e3cf77c45020.

Analysis of ‘Life history strategies of stream fishes linked to watershed hydrology’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/69067ebb-8809-4b32-bc1c-752087cb96ae on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Open Access Data. Hitt, NP, Landsman, AP, and Raesly, RL. Life history strategies of stream fishes linked to watershed hydrology.

--- Original source retains full ownership of the source dataset ---

Abstract

It was derived from the Gippsland DEPI Stream Gauge Data dataset (GUID: 138db7d6-ba9e-4b40-a351-4912db7328b5). You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The dataset shows the spatial location of stream gauges within the Gippsland region with site details including the period of data collection, catchment area and elevation are attached as attributes.

Dataset History

The dataset was created by:

1. Importing the Gippsland Stream Gauge Data (GUID: 138db7d6-ba9e-4b40-a351-4912db7328b5) 'Site Details.csv' into ESRI ArcMap 10.2.

2. Displaying the data by the Latitude and Longitude fields

3. Exporting the data to an ESRI shapefile

Dataset Citation

Bioregional Assessment Programme (2014) Gippsland Basin Stream Gauge Locations. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/112c9bd3-98c1-4141-9c41-9068480d3098.

Dataset Ancestors

• Derived From Gippsland DELWP stream gauge data

Analysis of ‘Life history strategies of stream fishes linked to watershed hydrology’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/895ddc4b-7725-4546-ad3b-bf74f5fddae6 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Open Access Data. Hitt, NP, Landsman, AP, and Raesly, RL. Life history strategies of stream fishes linked to watershed hydrology.

--- Original source retains full ownership of the source dataset ---

The Siple Dome Glaciology and Ice Stream History project was part of Western Divide West Antarctic Ice Cores (WAISCORES), an NSF-funded project to understand the influence of the West Antarctic ice sheet on climate and sea level change. WAISCORES researchers acquired and analyzed ice cores from the Siple Dome, in the Siple Coast region, West Antarctica.

This project supported glaciological studies of Siple Dome and its surroundings between Ice Streams C and D, via two major goals. First, it sought to characterize the dynamic environment and ice stratigraphy of Siple Dome and its surroundings, with the specific mission of assessing Siple Dome as a potential deep core site; and second, to determine whether the configuration of ice stream flow in the region has changed over time. Both goals are relevant to understanding the dynamics of the West Antarctic Ice Sheet (WAIS), its history, and potential future behavior.

This project was a collaboration between Saint Olaf College, the University of Washington, and the National Snow and Ice Data Center at the University of Colorado. It included studies of satellite imagery and acquisition and analysis of field data from GPS, firn cores and snow pits, and ground-based ice-penetrating radar.

Data in this collection were obtained during two Antarctic field seasons in 1994–95 and 1996–97. The data set is available via FTP as Microsoft Excel Spreadsheet (.xls) and ASCII tab delimited (.txt) files. Related notes are available as a Microsoft Word (.doc) or text (.txt) file. Related images and charts are available as Graphics Interchange Format (.gif) and Joint Photographic Experts Group (.jpg) files.

Introduction This dataset records all curtailment events experienced by curtailable-connection customers. About Curtailment When a generation customer requests a firm connection under a congested part of our network, there may be a requirement to reinforce the network to accommodate the connection. The reinforcement works take time to complete which increases the lead time to connect for the customer. Furthermore, the customer may need to contribute to the cost of the reinforcement works.UK Power Networks offers curtailable-connections as an alternative solution for our customers. It allows customers to connect to the distribution network as soon as possible rather than waiting, and potentially paying, for network reinforcement. This is possible because under a curtailable connection, the customer agrees that their access to the network can be controlled when congestion is high. These fast-tracked curtailable-connections can transition to firm connections once the reinforcement activity has taken place. Curtailable connections have enabled faster and cheaper connection of renewable energy generation to the distribution network owned and operated by UK Power Networks.The Distribution System Operator (DSO) team has developed the Distributed Energy Resource Management System (DERMS) that monitors curtailable-connection generators as well as associated constraints on the network. When a constraint reaches a critical threshold, an export access reduction signal may be sent to generators associated with that constraint so that the network can be kept safe, secure, and reliable.This dataset contains a record of curtailment actions we have taken and the resultant access reduction experienced by our curtailment-connections customers. Access reduction is calculated as the MW access reduction from maximum × duration of access reduction in hours (MW×h). The dataset categorises curtailment actions into 2 categories: Constraint-driven curtailment: when a constraint is breached, we aggregate the access reduction of all customers associated with that constraint. A constraint breach occurs when the network load exceeds the safe limit. Non-constraint driven curtailment: this covers all curtailment which is not directly related to a constraint breach on the network. It includes customer comms failures, non-compliance trips (where the customer has not complied with a curtailment instruction), planned outages and unplanned outages Each row in the dataset details the start and end times, durations and customer access reduction associated with a curtailment actions. We also provide the associated grid supply point (GSP) and nominal voltage to provide greater aggregation capabilities. By virtue of being able to track curtailment across our network in granular detail, we have managed to significantly reduce curtailment of our curtailable-connections customers. Methodological Approach A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output and mode of operation. RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status. The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP.DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator. Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated. Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously. Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches. Where customer curtailment occurs without any associated constraint breach, we categorise the curtailment as non-constraint driven. Future developments will include the reason for non-constraint driven curtailment. Quality Control Statement The dataset is derived from data recorded by RTUs located at customer sites and constraint locations across our network. UKPN’s Ops Telecoms team monitors and maintains these RTUs to ensure they are providing accurate customer/network data. An alarms system notifies the team of communications failures which are attended to by our engineers as quickly as possible. RTUs can store telemetry data for prolonged periods during communications outages and then transmit data once communications are reinstated. These measures ensure we have a continuous stream of accurate data with minimal gaps. On the rare instances where there are issues with the raw data received from DERMS, we employ simple data cleaning algorithms such as forward filling. RTU measurements of access reduction update on change or every 30-mins in absence of change. We also minimise postprocessing of RTU data (e.g. we do not time average data). Using the raw data allows us to ascertain event start and end times of curtailment actions exactly and accurately determine access reductions experienced by our customers. Assurance Statement The dataset is generated and updated by a script which is scheduled to run daily. The script was developed by the DSO Data Science team in conjunction with the DSO Network Access team, the DSO Operations team and the UKPN Ops Telecoms team to ensure correct interpretation of the RTU data streams. The underlying script logic has been cross-referenced with the developers and maintainers of the DERMS scheme to ensure that the data reflects how DERMS operates. The outputs of the script were independently checked by the DSO Network Access team for accuracy of the curtailment event timings and access reduction prior to first publication on the Open Data Portal (ODP). The DSO Operations team conduct an ongoing review of the data as it is updated daily to verify that the operational expectations are reflected in the data. The Data Science team have implemented automated logging which notifies the team of any issues when the script runs. This allows the Data Science to investigate and debug any errors/warnings as soon as they happen.

Other

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

A historical conditions MODFLOW6 and SNTEMP model was developed to simulate groundwater flows and instream temperatures in Beaver Creek, Alaska during the study period and time just prior to the study. Model parameter estimation was performed via history matching of groundwater levels, instream flows and stream temperatures for 2019–2023. This data release contains a version of the historical model built from the best parameter set generated during the history matching. The history matching and model development workflows are archived in the "Groundwater flow and SNTEMP stream temperature model build and history matching workflows" child item associated with this data release. The version of the model included in this child item is the version that was used to generate the historical model results presented in the associated publication "Simulating present and future Groundwater/Surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska". This data release includes the model input files, executable files, and output files for that historical model.

AWS Spot Price History

This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.

Data format

Each month of data is stored as a ZStandard-compressed .tsv.zst file.

The data format matches that returned by AWS's describe-spot-instance-prices, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:

euc1-az2 i4i.8xlarge Linux/UNIX 1.231800 2023-02-28T23:59:57+00:00
euc1-az3 r5b.8xlarge Red Hat Enterprise Linux 0.749600 2023-02-28T23:59:58+00:00
euc1-az3 r5b.8xlarge SUSE Linux 0.744600 2023-02-28T23:59:58+00:00
euc1-az3 r5b.8xlarge Linux/UNIX 0.619600 2023-02-28T23:59:58+00:00
euc1-az3 m5n.4xlarge Red Hat Enterprise Linux 0.476000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge Red Hat Enterprise Linux 0.492000 2023-02-28T23:59:59+00:00
euc1-az3 m5n.4xlarge SUSE Linux 0.471000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge SUSE Linux 0.487000 2023-02-28T23:59:59+00:00
euc1-az3 m5n.4xlarge Linux/UNIX 0.346000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge Linux/UNIX 0.362000 2023-02-28T23:59:59+00:00

When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:

euw3-az2 g4dn.4xlarge Linux/UNIX 0.558600 2023-01-01T00:00:00+00:00

For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.

Using data

You can process each month individually. If you need the entire data stream at once, you can cat all files to zst together:

cat prices/*/*.tsv.zst | zstd -d

1. In streams, hydrology is a predominant driver of ecological structure and function. Providing adequate flows to support aquatic life, or environmental flows, is therefore a top management priority in stream systems. 2. Flow regime classification is a widely accepted approach for establishing environmental flow guidelines. However, it is surprisingly difficult to quantify relationships between hydrology and ecology (flow-ecology relationships) while describing how these relationships vary across classified flow regimes. Developing such relationships is complicated by several sources of spatial bias, such as autocorrelation due to spatial design, flow regime classification, and other environmental or ecological sources of spatial bias. 3. We used mixed moving-average spatial stream network models to develop flow-ecology relationships across classified flow regimes and to assess spatial patterns of these relationships. We compared relationships between fish traits and life-history strategies with hydrologic metrics across flow regimes and assessed whether spatial autocorrelation influenced these relationships. 4. Trait-hydrology relationships varied between flow regimes and across all streams combined. Some relationships between traits and hydrologic metrics fit predictions based on life-history theory, while others exhibited unexpected relationships with hydrology. Spatial factors described a large proportion of variability in fish traits and different patterns of spatial autocorrelation were observed in different flow regimes. Synthesis and Applications. Further work is needed to understand why flow-ecology relationships vary across classified flow regimes and why these relationships may not fit predictions based on life-history theories. Managers determining environmental flow standards need to be aware that different hydrologic metrics are often important drivers of fish trait diversity in different flow-regimes. Flow-ecology relationships may therefore be confounded by spatial structure that is inherent in flow regime classification and much existing biological data. Complex patterns of spatial bias should be considered when managing stream systems within an environmental flows framework.

Complete list of taxa and total density (ind/m2) of the Chironomidae taxa collected in the river Saltana (Ecuador). Pod: Podonomus; Paro: Parochlus; Limay: Limaya; C. sp3: Cricotopus sp3.; Oliv: Cricotopus (Oliveiriella) rieradevallae; Gnost: Genus Nostoc; G1: Genus 1 sp a. CrMR: Cricotopus spMR; Cric spp: Cricotopus spp; Lymnp: Lymnophyes; Parakieff: Parakiefferiella; Metrioc: Metriocnemus; Paraph: Paraphaenocaldius; Parametric: Parametriocnemus; GenNear Parm: Genus near Parametriocnemus; Tanytarsini: Tanitarsini. II, III, IV correspond to larvae instar.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

HUN AWRA-L Stream Network v01 depicts the 'blue line' drainage network used in the river model AWRA-R. It includes information on stream length for each subcatchment defined in the river model node-link network.

Purpose

AWRA-R requires stream length of each stream within a subcatchment in AWRA-R in order to estimate model parameters related to in-stream losses and routing.

Dataset History

HUN AWRA-L Stream Network v01 is created from a river map of NSW (described in the lineage) which is overlain and clipped by a subcatchment map, which in turn is defined by a subset of streamflow gauges. Only the river network between upstream and downstream gauges (may have multiple upstream gauges) are considered, the rest are manually selected and removed.

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-L Stream Network v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/ec50a7fd-0bda-4a82-9de7-3fa2a9065bdb.

Dataset Ancestors

• Derived From SYD ALL Raw Stream Gauge Data BoM v01

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Hunter Surface Water data v2 20140724

• Derived From Selected streamflow gauges within and near the Hunter subregion

• Derived From SYD ALL Unified Stream Gauge Data v01

• Derived From HUN DEM derived catchment boundaries v01

• Derived From Hunter Surface Water data extracted 20140718

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From SSB Hydstra gauges v01