Provide the number of households in each administrative district of Taoyuan City for fire safety equipment inspection, the number of qualified and unqualified households, and other related penalty information.

CSV 602c6b57-55d7-4170-a57a-f207414ec9fd

This dataset contains Vegetation and Biomass monitoring data collected for the NESP Project 2.1.4 (Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands). The aim of the vegetation monitoring in relation to this project is to track change in biomass and species composition over time on hillslope areas above gully erosion within control and treatment sites (treatments vary), linking to changes in downstream water quality. Data is from control and treatment gully sites on commercial grazing properties in the Burdekin being monitored as part of NESP Project 2.1.4.

The data in presented in this metadata are part of a larger collection and are intended to be viewed in the context of the project. For further information on the project, view the parent metadata record: Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands (NESP TWQ 2.1.4, CSIRO)

Monitoring of these sites is continuing as part of NESP TWQ Project 5.9. Any temporal extensions to this dataset will be linked to from this record.

BOTANAL files describe the biomass, species composition and species attributes such as basal area and cover for hillslope areas above gully erosion sites. PATCHKEY files describe the landscape condition (proportional) of vegetation patches for hillslope areas above gully erosion sites. And BIOMASS files describe the cover and biomass within the gully.

Methods: Vegetation metrics were measured on the hillslope above each of the NESP gullies at the end of the dry season (‘EOD’, October–November) and then again at the end of the wet season (‘EOW’, April). Measurements were initiated in November 2016 at all sites except Mt Wickham which started in August 2018. Landscape and vegetation condition transects were installed upslope of the uppermost head section on both the treated and control gullies at all sites (Figure 8). Transects were run along slope contours and varied in length and spacing for each site depending on gully-head catchment size. Four to five transects were used at each treatment and control location. Each transect has a permanent marker at the beginning and end to facilitate repeat measures. Pasture metrics were recorded along each transect using a 1m2 quadrat based on the methods of Tothill et al., (1992), with placement of quadrats dependent on transect length at each site (30 quadrats were sampled for each treatment/control area). Metrics included the main pasture species and/or functional group composition and frequency, above-ground pasture biomass (DMY), total cover, litter cover, basal-area %, defoliation level and key soil surface condition metrics (Tongway and Hindley, 1995). In addition landscape condition was calculated along each transect using PATCHKEY (Abbott and Corfield, 2012). The condition assessments were aggregated to reflect ABCD landscape condition as used across grazed landscapes in the GBR catchments (Aisthorp and Paton, 2004; Chilcott et al., 2005; Bartley et al., 2014). Cover and biomass estimates were calibrated against standard quadrats taken at each site using classified quadrat photographs. Biomass standards were oven dried to attain dry matter yield, removing vegetation water retention error between treatments. A real time kinematic (RTK) survey ran from upslope of the vegetation survey to the valley section for each gully system. Gully vegetation cover and biomass were also measured within each gully, on the gully walls and gully floor. Sampling was initiated at the end of the first wet season (April 2017) for all sites except Mt Wickham which started in August 2018. The sampling methodology was very similar to the hillslope survey. A minimum of three transects were measured across each gully, representative of the head, middle and valley sections. At each transect, % cover, biomass and dominant cover type was assessed using 0.25 m2 (0.5 x 0.5m) quadrats. Three quadrats were assessed on each wall (six in total) and six quadrats assessed in the deepest part of the channel in the gully floor. Box plots of the % cover and biomass data at the end of each wet season for control and treatment sites were analysed using Sigmaplot Version 14. In most cases a t-test for means and non-parametric Mann-Whitney rank sum test were conducted to evaluate differences between treatment and control sites

PATCHKEY data was collected at 5 parallel fixed transects above gully locations (hillslope area) – length and spacing of transects varied between sites dependant on hillslope size. PATCHKEY data was collected digitally using custom software on handheld android device. Survey occurs each year before and after wet season. Biomass is calibrated against cut and dried samples and cover is calibrated against classified quadrat photos – per collector. PATCHKEY is used to derive landscape condition from vegetation/grazed patches using vegetation and soil components to derive a condition state – it can be directly aggregated to match ABCD landscape condition. Condition state changes can be measured over time and related to water quality downstream.

BOTANAL is a comprehensive sampling and computing procedure for estimating pasture biomass and species composition (Tothill et al 1992) – these files describe the biomass, species composition and species attributes such as basal area and cover for hillslope areas above gully erosion sites.

Limitations of the data: This dataset contains Vegetation monitoring data collected at these gully sites for end of the dry season (‘EOD’, October–November) for Hillslope only and then again at the end of the wet season (‘EOW’, April) for Hillslope and Gully over three reporting periods 2016-2017, 2017-2018 and 2018-2019. Measurements were initiated in November (EOD) 2016 at all sites except Mt Wickham which started in August (EOD) 2018.

Format:

This data collection consists of 3 zip files each monitoring period. Each zip file contains 4 CSV files and one Microsoft Excel file: Two BOTANAL CSV files – 1 EOD and 1 EOW, Two PATCHKEY CSV files – 1 EOD and 1 EOW

End of Wet season Gully_veg measurements are stored by season as Microsoft Excel file. Individual tabs for each site record the raw data as captured in the field. The “Stnds” tab calculated the calibration from BIO code to actual biomass. The “all_for_stats” sheet is the intermediate sheet for collation of data in “Wall” and “Channel” sheets ready for analysis in stats package. “Summary” tab contains summary data for the report.

Data Dictionary:

BOTANAL CSV headers: ID: Unique identifier for sample USER: Collector name/initials SITE: Site name (abbreviation) TRAN: Transect number (numbered from nearest to gully) QUAD: Quadrat number per transect (numbered from left side – looking downhill) DATE: Date and time SP1 to SP5: Species name abbreviated using first two letters of genus and first three of species name eg. Bothriochloa pertusa = boper. Species recorded in order of highest biomass represented. SPP1 to SPP5: Species proportion by weight of quadrat for each of species 1 to 5 YIELD: Total biomass estimate for quadrat in kg/ha DEFOL: Estimated cattle defoliation of the pasture within quadrat. Categorical – 1=0-5%, 2=5-25%, 3=25-50%, 4=50-75% and 5=75-100% BASAL: Estimated basal area of Perennial tussock grasses. % of quad COVER: Foliage projected cover % of quad LITTER: Litter cover % of quad BARE: Bare ground % of quad (optional) HARD: Soil hardness (after Tongway et al – landscape functional analysis). Categorical – 1=Easily broken, 2=Moderately hard, 3=Very hard, 4=Sand, 5=Self mulching. DEPOS: Deposition from erosion processes, Categorical – 1=Insignificant, 2=slight, 3=moderate, 4=extensive INCORP: Litter incorporation into soil surface. Categorical – 1=nil, 2=low, 3= moderate, 4=high EROSION: Categorical – 1=Insignificant, 2=slight, 3=moderate, 4=extensive COMMENT: Any comments about individual quadrat sample.

PATCHKEY CVS Headers: ID: Unique identifier for sample RECORDER: Collector name/initials SITE: Site name (abbreviation) TRAN: Transect number (numbered from nearest to gully) PATCH_NO: Number of the patch occurring along a transect DATE: Date and time DOMINANT: Dominant vegetation functional group within a patch. See manual for categories BASAL: Estimated basal area of Perennial tussock grasses. % of quad LITTER: Litter cover % of quad YIELD: Total biomass estimate for quadrat in kg/ha WOODY: Presence of woody regrowth BARE: Bare ground % of patch EROSION: Categorical – 1=Insignificant, 2=slight, 3=moderate, 4=extensive HARDNESS: Soil hardness (after Tongway et al – landscape functional analysis). Categorical – 1=Easily broken, 2=Moderately hard, 3=Very hard, 4=Sand, 5=Self mulching. INCORPORATION: Litter incorporation into soil surface. Categorical – 1=nil, 2=low, 3= moderate, 4=high PATCH_TYPE: Patch type classification auto-calculated in software from inputs PATCH_EST: Patch type estimated by user – overrides calculated value LAT: Latitude – if used with differential GPS can auto calculate patch length (optional) LON: Longitude - if used with differential GPS can auto calculate patch length (optional) PATCH_LENGTH: Measured patch length along transect ACC: Accuracy of GPS coordinates COMMENT: Any comments about individual patch samples

Fieldnames used in Gully_Veg XLSX spreadsheets: Date – date of measurement Quad – quadrat measured (not always numbered) Loc – location on gully –cross sections from RTK. Numbers are in order from 1 nearest incrementing by 1 downstream Pos – walls (left bank (lb), right bank (rb)) or channel BIO – biomass code from 0 to 5 – this is a surveyor-specific estimate which is calibrated to actual biomass using standards Cov – estimate of percent cover Sp – species composition COMMENT – any comments relating to the quadrat or

This dataset contains key characteristics about the data described in the Data Descriptor Fast micro-computed tomography data of solute transport in porous media with different heterogeneity levels. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Soil pollution is a pressing problem requiring solutions that can be applied without large-scale side effects directly in the field. Phytoremediation is an effective strategy combining plant and root-associated microbiome to immobilize, degrade, and adsorb pollutants from the soil. To improve phytoremediation, it is necessary to think of plants, fungi, and bacteria not as individual entities, but as a meta-organism that reacts organically, synergistically, and cooperatively to environmental stimuli. Analyzing the tripartite enzymatic activity in the rhizosphere is necessary to understand the mechanisms underlying plant–microorganism communication under abiotic stress (such as soil pollution). In this work, the potential of a microbial consortium along with a plant already known for its phytoremediation capabilities, Schedonorus arundinaceus (Scheb.) Dumort., was validated in a mesocosm experiment with pluricontaminated soil (heavy metals, PAHs, and PCBs). Chemical analyses of the soil at the beginning and end of the experiment confirmed the reduction of the main pollutants. The microscopic observation and chemical analyses confirmed the greater root colonization and pollutant removal following the microbial treatment. To obtain a taxonomic and functional picture, tripartite (plant, fungi, and bacteria) enzyme activity was assessed using a metatranscriptomic approach. Total RNA was extracted from a sample of rhizosphere sampled considering 2 centimeters of root and soil attached. From the total reads obtained, mRNAs were filtered, and analysis focused on reads identified as proteins with enzymatic activity. The differential analysis of transcripts identified as enzymes showed that a general increase in potential enzyme activity was observed in the rhizosphere after our biotechnological treatment. Also from a taxonomic perspective, an increase in the activity of some Phyla, such as Actinobacteria and Basidiomycota, was found in the treated sample compared to the control. An increased abundance of enzymes involved in rhizospheric activities and pollutant removal (such as dehydrogenase, urease, and laccase) was found in the treated sample compared to the control at the end of the experiment. Several enzymes expressed by the plant confirmed the increase in metabolic activity and architectural rearrangement of the root following the enhancement of the rhizospheric biome. The study provides new outcomes useful in rhizosphere engineering advancement.

This dataset contains information obtained by pyrosequencing genomic DNA extracts from soil with PCR primers targeting the bacterial 16S gene (27F/519R) and fungal ITS region (ITS1/ITS4-B). The data were processed in a pipeline using freely available 'mothur' software (v1.24.1). The reads were processed in 4 ways, this was a combination of subsampling the data to a number that normalised all but the 10 lowest samples and excluding operational taxonomic units (OTUs) that only occurred once in the entire dataset. This resulted in designations FULL_READS for the unsubsampled analyses, and SUBSAMPLED for those subsampled. Then SINGLETONS_INCLUDED (or SING_INC) for analyses where singleton OTUs were included and SINGLETONS_EXCLUDED (or SING_EXC) for those where dataset wide singletons were removed. For each analysis, this produced a .fasta (sequence info), .names (sequence redundancies) and a .groups (sequence to sample assignment) (in the chimera checked data fo lders)

For each of these combinations an OTU abundance matrix was generated that has a .shared extension, which is a table of OTU by samples and the corresponding abundance of the OTU. Various alpha and beta diversity measure were calculated for each analysis, including diversity indices (extension .groups.summary), catchall (various .csv files), rarefaction data for each sample (extension .rarefaction), relative and species abundance data (extension .rabund and .sabund), unifrac community similarity measures (contained in the unifrac folder, are a distance matrix of the sample-by-sample dissimilarity, and a list .summary of the dissimilarities, in addition a neighbour-joining tree of the entire dataset from which the unifrac measures are calculated).

In addition to these diversity measures, taxonomy was defined by bayesian searching of each OTU sequence against the GreenGenes database (2011 version, McDonald et al 2011, ISME J) this is provided as a .taxonomy and .summary file in the taxonomy folders. Also representative sequences for each OTU are in the OTU_rep folders as .fasta and .names files.

The raw data are provided in the preprocessing files folders.

Trace elements in soils from Guadiamar River Basin (SW Spain), year 2014This data set contains information on chemical properties of soils from the Guadiamar River Valley (SW Spain). This set corresponds to a series of environmental surveys conducted in the Guadiamar River Valley to monitor de fate of contaminating trace-elements in soils after a huge mine accident that released acid waters and trace-element contaminated sludge into the Guadiamar River basin. Data included in this package were gathered between February and March 2014, 16 years after the mining accident (occurred in April 1998) and subsequent remediation programme. It includes soil data (general properties and quasitotal and soluble trace element concentrations) from 20 sampling sites along the Guadiamar River basin, with three topographic locations along the river section sampled at each of the 20 sites.GUADIAMAR_TE2014.csv

The data was collected by Lisa Meyer with a Leica1200 RTK dGPS unit loaned from the Australian Antarctic Divisions Science Branch. Point data was collected within the Macquarie Island station limits to enable accurate mapping of the existing and newly installed infrastructure, as well as sampling sites, associated with the Remediation Program.

Data included: 1. Infrastructure - the water sampling sites (mini/piezometers and seeps), soil sampling sites for the 2014-15 season (Annual pits, Environment Protection Authority sampling sites), aeration manifolds, the Permeable Reactive Barriers and drainage channels installed at the Main Power House (MPH) remediation site; 2. Building boundaries, footpaths, fence lines around the Fuel Farm and MPH, and any other permanent features close to, or closely associated with the Remediation infrastructure. 3. External position of the newly built Machinery Shed (a.k.a. the helicopter shed during station resupply) for mapping by the AADC. 4. Height data for the isthmus area relating to the remediation work. These are referred to as 'spot heights' and are not useful to generate contour maps, but rather give some general idea as to the terrain around sampling sites and potential transport pathways from the isthmus area to the adjacent ocean. 5. State Permanent Markers (10708, 10709, AUS211-RM3). 6. Base station data collected at permanent survey marker NMX1. 7. Surveying points used by Parks and Wildlife Service Tasmania, to assess the location of the shoreline around the isthmus.

Data available include: 1. GPS raw data downloaded from the Leica GPS unit. There are three field survey files (one for each day's surveying). There are also three separate base station data files, associated with each field survey. This data requires the program Leica Geo Office to visualise the data and export it (LGO has a free download that can be used). Note: see comments in Q.7. 2. Updated raw data files - Three CSV files of the updated raw data, created by Scott Strong (DPIPWE) using the full version of Leica Geo Office. The datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected - Universal Transverse Mercator Zone 57. 3. A PDF file showing duplicate Point ID's that were changed in the three LGO projects. 4. Scott Strong shapefiles - Three ArcGIS shapefiles of the field data. These files were used to create individual shapefiles for separate features e.g. 'PRB infrastructure', because each of the field data files contains data from the entire days surveying. 5. Updated shapefiles - (i) The Scott Strong shapefiles copied and renamed with "_ITRF2000" in the name. (ii) The Scott Strong shapefiles copied and the data shifted to match the station WGS84 datum and shapefiles renamed with "_WGS84" in the name. See further details below.

Scott Strong generated three shapefiles of the original field data, which were named: Macca02122014FieldSurvey Macca05122014FieldData Macca06122014FieldData These three files are in 'AAD Macquarie Island Dec 2014.zip'. The datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected UTM Zone 57.

These shapefiles were renamed to: Macca02122014FieldData_ITRF2000 Macca05122014FieldData_ITRF2000 Macca06122014FieldData_ITRF2000

Note: The AADC uses the WGS84 datum from the mid-1990s for previously surveyed data of Macquarie Island. To transform data surveyed on ITRF2000@2000 to WGS84 apply "The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N." given on page 3 of the survey report "Macquarie Island OSG Survey Campaign, Voyage 8 Round Trip, March 2002" by John VanderNiet and Nick Bowden. i.e. the eastings of the WGS84 data will be 1.40 metres greater than the ITRF2000@2000 data and the northings of the WGS84 data will be 0.20 metres greater than the ITRF2000@2000 data.

A copy of the ITRF2000 shapefiles was created and edited in ArcMap to shift the data points to match the station WGS84 reference frame (i.e. by subtracting -1.4 m from the eastings and -0.2 m from the northings). The projection data is also changed to WGS84, rather than ITRF2000.

These shapefiles are named: Macca02122014FieldData_WGS84station Macca05122014FieldData_WGS84station Macca06122014FieldData_WGS84station

The data point names are not changed in the WGS84 shapefiles - the points that were averaged in the raw data, which Scott changed to his codes. e.g. NW1SS1 (point is called NW1 Scott Strong1). NW1 (i.e. north west 1) is a corner on the FF bund and also a point up in the Power House area somewhere (PRB mini cage).

A Point Description (Point_Desc) field was added to the attribute tables of the WGS84 shapefiles to explain what the point codes are for each of the data points.

The Macca06122014FieldData_WGS84station shapefile includes points collected in a survey of the isthmus shoreline at the request of Chris Howard of the Tasmanian Parks and Wildlife Service. These inclu...

Phytomanagement employs vegetation and soil amendments to reduce the environmental risk posed by contaminated sites. We investigated the distribution of trace elements in soils and woody plants from a large phytomanaged site, the Guadiamar Valley (SW Spain), 7 years after a mine spill, which contaminated the area in 1998. At spill-affected sites, topsoils (0-25 cm) had elevated concentrations of As (129 mg kg(-1)), Bi (1.64 mg kg(-1)), Cd (1.44 mg kg(-1)), Cu (115 mg kg(-1)), Pb (210 mg kg(-1)), Sb (13.8 mg kg(-1)), Tl (1.17 mg kg(-1)) and Zn (457 mg kg(-1)). Trace element concentrations in the studied species were, on average, within the normal ranges for higher plants. An exception was white poplar (Populus alba), which accumulated Cd and Zn in leaves up to 3 and 410 mg kg(-1) respectively. We discuss the results with regard to the phytomanagement of trace element contaminated sites.

Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]

This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript.

The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).

Data were collected in association with the removal of PCB contaminated sediment during the Manistique River Area of Concern Operable Unit 2 remediation construction efforts. Data collected included the following: (i) Water column turbidity readings acquired using a YSI PRO DSS - handheld multi-parameter instrument for documenting potential impacts to water quality during construction activities; (ii) Sediment confirmation samples acquired using a Vibracore and Ponar Dredge for documenting PCB levels remaining in the sediment following the completion of dredging (and prior to placement of cover material); (iii) Air samples acquired using a Leland Legacy Low Volume Sampler for documenting potential PCB impacts to the community during construction activities; (iv) Air monitoring samples acquired using a DustTrak II 8530 for documenting potential particulate dust impacts to the community during construction activities. Pace Environmental Laboratories and Eurofins TestAmerica Burlington analyzed the sediment samples and air samples, respectively, for PCB. Most of the results from this work are presented in csv file format, with XML, Microsoft Word, and PDF/a files also included. This project was funded under NOAA Grant Nos. NA13NMF4630257 and NA19NMF4630008. The project partners involved are the State of Michigan Department of Technology, Management, and Budget (DTMB), Michigan Department of Environment, Great Lakes, and Energy (EGLE), United States Environmental Protection Agency (USEPA), United States Army Corps of Engineers (USACE) and the National Oceanic and Atmospheric Administration (NOAA). Construction work was completed by White Lake Dock and Dredge, Inc. and Arcadis of Michigan, LLC who were the State of Michigan’s design and oversight consultant.

This dataset contains Vegetation and Biomass monitoring data collected for the NESP TWQ Project 5.9, formally NESP TWQ 2.1.4 - Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands and Landholders Driving Change (LDC) contracts LRP17-003 and LME17-009. Data is from control and treatment gully sites on commercial grazing properties in the Burdekin.

BOTANAL files describe the biomass, species composition and species attributes such as basal area and cover for hillslope areas above gully erosion sites.

PATCHKEY files describe the landscape condition (proportional) of vegetation patches for hillslope areas above gully erosion sites.

GULLY_VEG describes the biomass, species composition and species attributes such as percent cover for locations within the Gully.

Seven paired Control/Treatment gully sites on commercial grazing properties in the Burdekin being monitored as part of NESP Project 5.9 and NESP Project 2.1.4 (Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands) and Landholders Driving Change (LDC) contracts LRP17-003 and LME17-009. The key question being asked is "is there measureable improvement in the erosion and water quality leaving remediated gully sites compared to sites left untreated?" The monitoring approach uses a modified BACI (Before after control impact) design.

The aim of the vegetation monitoring in relation to this project is to track change in land condition and vegetation over time on hillslope areas above, and within gullies within control and treatment sites (treatments vary). Linking to changes in downstream water quality.


Methods:

Vegetation metrics were measured on the hillslope above and within each of the monitored gullies at the Pre-wet or end of the dry season ('EOD', October–November) and then again at the post-wet or end of the wet season ('EOW', April). Measurements were initiated in November 2016 at all sites except Mt Wickham which started in August 2018 and at Mt Pleasant and Glen Bowen that began in November 2019.

For BOTANAL and PATCHKEY, Landscape and vegetation condition transects were installed upslope of the uppermost head section on both the treated and control gullies at all sites. Transects were run along slope contours and varied in length and spacing for each site depending on gully-head catchment size. Four to five transects were used at each treatment and control location. Each transect has a fixed beginning and end to facilitate repeat measures. Length and spacing of transects varied between sites dependant on hillslope size. 6 to 8 x 1m quadrats were sampled along each transect at equal spacing giving 30 to 32 samples per site (this may vary due to some changes in study sites over time – eg. New fence placements or inaccessibility due to weather). See "Interactive map of this dataset" in the online resources for layouts at sites.

BOTANAL data was collected along each transect using a 1m² quadrat using the methods of Tothill et al., (1992), with placement of quadrats dependent on transect length at each site (30 quadrats were sampled for each treatment/control area). Metrics included the main pasture species and/or functional group composition and frequency, above-ground pasture biomass (DMY), total cover, litter cover, basal-area %, defoliation level and key soil surface condition metrics (Tongway and Hindley, 1995).

In addition landscape condition was calculated along each transect using PATCHKEY (Abbott and Corfield, 2012). The condition assessments were aggregated to reflect ABCD landscape condition as used across grazed landscapes in the GBR catchments (Aisthorp and Paton, 2004; Chilcott et al., 2005; Bartley et al., 2014). Cover and biomass estimates were calibrated against standard quadrats taken at each site using classified quadrat photographs. Biomass standards were oven dried to attain dry matter yield, removing vegetation water retention error between treatments. A real time kinematic (RTK) survey ran from upslope of the vegetation survey to the valley section for each gully system. Data was collected at 4 to 5 parallel fixed transects above gully locations (hillslope area) – length and spacing of transects varied between sites dependant on hillslope size. See "Interactive map of this dataset" in the online resources for transect locations at sites. PATCHKEY data was collected digitally using custom software on handheld android devices. Survey occurred each year before and after wet season. Biomass is calibrated against cut and dried samples and cover is calibrated against classified quadrat photos – per collector.

GULLY_VEGetation cover and biomass were also measured within each gully, on the gully walls and gully floor. Sampling was initiated at the end of the first wet season (April 2017) for all sites except Mt Wickham which started in August 2018 and at Mt Pleasant and Glen Bowen that began in November 2019. The sampling methodology was very similar to the hillslope survey. A minimum of three transects were measured across each gully, representative of the head, middle and valley sections. At each transect, % cover, biomass and dominant cover type was assessed using 0.25 m² (0.5 x 0.5m) quadrats. Three quadrats were assessed on each wall (six in total) and six quadrats assessed in the deepest part of the channel in the gully floor. Box plots of the % cover and biomass data at the end of each wet season for control and treatment sites were analysed using Sigmaplot Version 14. In most cases a t-test for means and non-parametric Mann-Whitney rank sum test were conducted to evaluate differences between treatment and control sites.


Limitations of the data:

This dataset contains Vegetation monitoring data collected at these gully sites for Pre-wet or end of the dry season ('EOD', October–November) for Hillslope only and then again post-wet of end of the wet season ('EOW', April) for Hillslope and Gully over four reporting periods 2016-2017, 2017-2018, 2018-2019 and 2019-2020. Measurements were initiated in November (EOD) 2016 at all sites except Mt Wickham which started in August (EOD) 2018 and at Mt Pleasant and Glen Bowen that began in November 2019.


Format:

This data collection consists of 4 zip files.

BOTANAL.zip contains eight CSVs containing pre-wet and post-wet survey data for all sites for dates between 2016 and 2020. In addition there is a species (SPP) decode CSV.

PATCHKEY.zip contains eight CSVs containing pre-wet and post-wet survey data for all sites for dates between 2016 and 2020.

GULLY_VEG.zip contains 4 XLSX files containing post-wet gully vegetation survey data for all sites for dates between 2016 and 2020. Post-Wet end of wet (EOW). Individual tabs for each site record the raw data as captured in the field. The "Stds" tab calculated the calibration from BIO code to actual biomass. The "all_for_stats" sheet (or "All End of Wet" in file "Gully_Veg_2017-2018.xlsx") is the intermediate sheet for collation of data in "Wall" and "Channel" sheets ready for analysis in stats package. "Summary" tab contains summary data for the report.

Veg_Spatial.zip consists of three shapefiles, Veg_transect_Locations.shp and Veg_Quadrat_locations.shp contain the transects and quadrat locations respectively in MGA94 Z55 coordinates for the BOTANAL and PATCHKEY surveys. Gully_Veg_locations.shp contains locations of the GULLY_VEG gully vegetation surveys in EPSG:4326 coordinates.


Data Dictionary:

Site Codes are as follows:
- MIN = Minnievale
- MV = Meadowvale
- MW = Mount Wickham
- SB = Strathbogie
- VP = Virginia Park
- MTP = Mount Pleasant
- GLB = Glen Bowen

In 2012, a program was initiated using in-stream and aerial (whole-watershed) liming to improve water quality and Brook Trout (Salvelinus fontinalis) recruitment in three acidified tributaries of a high-elevation Adirondack lake in New York State. Concurrently, macroinvertebrates were sampled annually between 2013 and 2016 at 3 treated and 3 untreated reference sites to assess the effects of each liming technique on this community. Macroinvertebrate communities were monitored at 6 study sites: T16, T8A (50 m upstream of lime application point), T8 (50 m downstream of lime application point), T6 (1230 m downstream of the lime application point), and at two unlimed reference streams, T24 and T20. T24 is of similar orientation, drainage area, discharge, and water chemistry as T16 and was selected as a reference site to assess the impacts of the watershed liming. T20 is a relatively well-buffered tributary that was monitored as a reference site for the in-stream liming effort. This dataset includes macroinvertebrate community data from 4-years (2013-2016) of macroinvertebrate sampling using artificial substrate basket samplers at six sites on tributaries to Honnedaga Lake, NY. Baskets were deployed in pairs at five stations (replicates) distributed longitudinally within each site (10 total baskets per site) and were placed on the bottom in pools where they were unlikely to become desiccated during water level fluctuations. Baskets were deployed between May 12 and May 16 and retrieved between July 10 and July 17 during each year, resulting in a colonization period of approximately two months. At the end of the colonization period, macroinvertebrates were extracted from each basket through a shaking and rinsing process. The contents from each pair of baskets were preserved together in 95-percent ethanol, resulting in 5 replicate samples collected from each site. A 200-organism subsample, or an exhaustive pick when less than 200 organisms were present, was sorted from each replicate using a gridded tray and identified to the lowest possible taxonomic resolution (usually genus or species). These identifications were then used to generate metrics of macroinvertebrate community condition for subsequent analyses. Data are provided in CSV and XLSX (MS Office 2013) format, a sample site location map is also provided (latitude/longitude datum and projection: NAD 1983 UTM Zone 18N).

File List

    Ambystoma.tigrinum.csv
    trends.rtf

Description
 trends.rtf is a text file with commands for implementing the quantile regression analyses on Ambystoma tigrinum trends in the text file Ambystoma.tigrinum.csv using the quantreg package in R.

    Ambystoma.tigrinum.csv
    Column 1: species - amphibian species,
    Column 2: year - year of count,
    Column 3: abundance - numbers per constant effort search,
    Column 4: lagabundance - abundance lagged 1 year.
    trends.rtf

This repository contains the full set of data (14 CSVs) used in the study "Investigating automated prompts to guide at-risk learners in an online practice platform". This study was a minor dissertation by Adam Reynolds in partial fulfilment of the requirements for the Master of Education (ICTs) from the University of Johannesburg.Data was collected using the Siyavula online adaptive practice platform during the second quarter of 2023. The CSV files in this collection are a set of linked tables containing the data at various points in the analysis pipeline and at different levels of aggregation.See README_DATA.md for more detailed descriptions of each table and column.See the associated code here.All data sets (and associated code) belong to the Siyavula Foundation and are released under a Creative Commons Attribution 4.0 International (CC-BY 4.0) license.

Working with the Geospatial Commission, the Geo6 organisations have produced a simplified common data catalogue providing core information on the geospatial datasets that they hold and manage. This catalogue or index of data is made available under OGL however, the underlying datasets may be available under different licences.

Geographic Coverage

England, Wales, Scotland

Additional Resources

• BGS

• CA

• HMLR

• OS

• UKHO

• VOA

Link to CSV column definitions

• Human Readable

• JSON schema

Frequency of Update

Bi-annual or sooner, if required.

In September 2017 the Agia Zoni II sank in the Saronic Gulf, Greece, releasing approximately 500 tonnes of heavy fuel oil, contaminating the Salamina and Athens coastlines. Effects of the spill, and remediation efforts, on sediment microbial communities were quantified over the following 7 months. Five days post-spill, the concentration of measured hydrocarbons within surface sediments of contaminated beaches was 1,093–3,773 μg g–1 dry sediment (91% alkanes and 9% polycyclic aromatic hydrocarbons), but measured hydrocarbons decreased rapidly after extensive clean-up operations. Bacterial genera known to contain oil-degrading species increased in abundance, including Alcanivorax, Cycloclasticus, Oleibacter, Oleiphilus, and Thalassolituus, and the species Marinobacter hydrocarbonoclasticus from approximately 0.02 to >32% (collectively) of the total bacterial community. Abundance of genera with known hydrocarbon-degraders then decreased 1 month after clean-up. However, a legacy effect was observed within the bacterial community, whereby Alcanivorax and Cycloclasticus persisted for several months after the oil spill in formerly contaminated sites. This study is the first to evaluate the effect of the Agia Zoni II oil-spill on microbial communities in an oligotrophic sea, where in situ oil-spill studies are rare. The results aid the advancement of post-spill monitoring models, which can predict the capability of environments to naturally attenuate oil.

To assess the performance of hazardous waste sites remediation technologies like in situ chemical oxidation (ISCO) with persulfate (S2O82−) researchers must periodically measure concentrations of target contaminants. Due to the presence of relatively high concentrations of the residual oxidant expected in many samples, the standard analytical method requires the addition of a relatively high concentration of ascorbic acid to prevent the oxidation process from continuing after sample collection. We discovered that addition of ascorbic acid quencher results in a radical chain reaction that transforms two common halogenated solvents (i.e., tetrachloroethene and hexachloroethane). To avoid the artifact associated with the radical chain reaction, a small quantity of n-hexane can be added to aqueous samples to extract target compounds and protect them from the radical chain reaction initiated by addition of the quencher. We..., , # Unwanted loss of volatile organic compounds (VOCs) during in situ chemical oxidation sample preservation: Mechanisms and solutions

Dataset DOI: 10.5061/dryad.g1jwstr34

Description of the data and file structure

This dataset contains raw and processed GC/MS data supporting Figures 1 and 2 in the associated manuscript, which examine unintended transformations of volatile organic compounds (VOCs) during sample preservation in in situ chemical oxidation (ISCO) experiments using persulfate and ascorbic acid. There are no blank cells in the data column. All values represent either detected signals.

Files and variables

File: JHML_Tae_Scavenger_Rawfile.xlsx

Figure 1 data: Artifact Formation During Preservation with Ascorbic Acid

The csv sheet titled **“Figure 1†** presents the experimental data demonstrating how the addition of ascorbic acid as a preservative in the presence of residual persulfate initiates a radical chain reaction tha...,

BackgroundResidual phoxim pollution presents a potential threat to natural ecosystems and human health. The immobilization of degrading strains on natural adsorbent materials is a common strategy to enhance the degradation of target compounds in the environment by the strains.MethodsA phoxim-degrading bacterial strain was isolated from the rhizosphere soil of rhubarb (Rheum palmatum L.), which had been exposed to long-term phoxim contamination. To enhance its stability and practical applicability, sodium alginate (SA) was utilized as a carrier material, while biochar (BC) and attapulgite (ATP) served as adsorption materials. These components were used to immobilize the strain, forming three distinct bacterial bead formulations: SA-RL4, SA + BC-RL4, and SA + ATP-RL4.ResultsThe isolated phoxim-degrading strain was identified as Novosphingobium sp. RL4. Furthermore, the degradation products of phoxim by strain RL4 were analyzed and characterized. Based on the specific surface area, mass-transfer performance results, adsorption isotherms, and degradation efficiency, the addition of ATP or BC to SA has an equally positive impact on the degradation of phoxim by immobilized microspheres. ATP can replace BC as an adsorbent carrier material for embedding bacteria to a certain extent. At 20 mg/L, SA + ATP-RL4 degraded 89.37% of phoxim in 72 h. Importantly, SA + ATP-RL4 can be reused, and the degradation efficiency remained above 80% after 5 cycles. Furthermore, it exhibits high tolerance and better degradation ability compared to free cells of RL4 when used in treating agricultural wastewater containing phoxim.ConclusionSA + ATP-RL4 shows potential for in situ remediation of phoxim-contaminated environments.