This data set, spanning the period 1980-2008, is assembled from (1) interpolation of observed monthly totals from available station records with bias adjustments (2) disaggregation of the monthly totals to daily totals, making use of daily precipitation forecasts from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis [Kalnay et al., 1996]. Station records from 1990 onwards are considered to be too sparse to assemble gridded products using this approach. A daily product from 1980 through the present is described separately (DAILY PRECIPITATION FROM STATISTICAL RECONSTRUCTIONS). This latter product, which is continually updated, makes use of a suite of variables from the NCEP/NCAR reanalysis. The data are presented in 24 sub-datasets of different spatial and temporal aggregations.

Question 1.4.8c: Does the SOE or government publicly disclose the date of the production sales executed by the SOE? , 1.4.6b: Does the SOE publicly disclose its aggregate sales volume?, 1.4.6a: Does the SOE publicly disclose its aggregate production volume?, 1.2.1a: Does the government publicly disclose data on the volume of extractive resource production?, 1.2.1c: Is the data disclosed on the volume of extractive resource production machine-readable?, 1.2.2c: Is the data disclosed on the value of extractive resource exports machine-readable?, 1.2.2b: How up-to-date is the publicly disclosed data on the value of extractive resource exports?

See the Related Datasets section for access to results of other disaggregation experiments conducted as part of this study.

These data were published in Song et al. (2023).
* This results publication includes additional supplementary material related to this dataset:
https://www.frontiersin.org/articles/10.3389/fmars.2023.1224518/full#supplementary-material

Full Description This dataset contains aggregate data concerning the number of unique children placed in open DCF placements at a single point in time - July 1st of each State Fiscal Year. These figures are disaggregated by Region, Gender, whether placement is in or out-of-state, and by the Type of Placement in which the child is residing on the observation date. The 'Other' Region category includes all cases that are not being served by a Regional DCF Office. This includes cases being served as/by Aftercare, General Administration, Treatment Services, Special Investigations Unit, DCF Hotline, or cases that have not been assigned to a DCF Regional office as of the date of observation. Note: Not every combination of filters will have values. CTData also carries this data disaggregated by Racial and Ethnic Groups and by Age Group. For more information, click the link below to see the full metadata.

Major differences from previous work: For level 2 catch: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines with identical strata but different effort units are duplicates reporting the same fishing activity with different measurement units. It is indeed not possible to infer strict equivalence between units, as some contain information about others (e.g., Hours.FAD and Hours.FSC may inform Hours.STD). in the case of WCPFC data, effort records were also kept in all originally reported units. Here, duplicates do not necessarily share the same “fishing_mode”, as SETS for purse seiners are reported with an explicit association to fishing_mode, while DAYS are not. This distinction allows SETS records to be separated by fishing mode, whereas DAYS records remain aggregated. Some limited harmonization—particularly between units such as NET-days and Nets—has not been implemented in the current version of the dataset, but may be considered in future releases if a consistent relationship can be established.

Under many aqueous conditions, metal oxide nanoparticles attract other nanoparticles and grow into fractal aggregates as the result of a balance between electrostatic and Van Der Waals interactions. Although particle coagulation has been studied for over a century, the effect of light on the state of aggregation is not well understood. Since nanoparticle mobility and toxicity have been shown to be a function of aggregate size, and generally increase as size decreases, photo-induced disaggregation may have significant effects. We show that ambient light and other light sources can partially disaggregate nanoparticles from the aggregates and increase the dermal transport of nanoparticles, such that small nanoparticle clusters can readily diffuse into and through the dermal profile, likely via the interstitial spaces. The discovery of photoinduced disaggregation presents a new phenomenon that has not been previously reported or considered in coagulation theory or transdermal toxicological paradigms. Our results show that after just a few minutes of light, the hydrodynamic diameter of TiO2 aggregates is reduced from ∼280 nm to ∼230 nm. We exposed pigskin to the nanoparticle suspension and found 200 mg kg−1 of TiO2 for skin that was exposed to nanoparticles in the presence of natural sunlight and only 75 mg kg−1 for skin exposed to dark conditions, indicating the influence of light on NP penetration. These results suggest that photoinduced disaggregation may have important health implications.

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.

The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has been clipped to the Gloucester PAE.

Dataset History

1.\tJoe Bell (GA) has clipped the surface water licences to the Gloucester PAE. This clip contains the works associated with the sharecomponent/ entitlement. This can be a many to many relationship. . A work is a surface water extraction point.

SW_licences_GloucesterPAE_Clip.dbf

2.\tShare component/ entitlement information was stored in the SW_Gloucester_COMBINED_v4.csv worksheet

3.\tTotal volume of share component/ entitlement is 10,786ML

4.\tThe works and share/component information was joined using Access, linking the CWlicence to the WAorCA_link. This links the volumetric entitlement to the works location.

5.\tThis link also created share components that had a 0 entitlement which are licences that have been converted to unbundled licences in the new Water Act

6.\tBy filtering out the 0 entitlement, the number of works linked to a share/component or entitlement with a specified volume was 212 with a total of 10,786ML. Worksheet FilteredIndividualSWLicences

7.\tWhere there was more than one works per licence, an additional column was add COUNT_CWLICENSE. This shows where the share component/ entitlement is double counted as it is matched to each work with the full allocation.

8.\tAn additional column was added SHARE_PER_WORKS which divides the share component/ entitlement by the number of works to give an allocation per works.

9.\tThe SHARE_PER_WORKS column allows you to plot the works with the share component in ArcGIS without double counting the allocation.

1. A glossary of terms used ini the water licensing is included here: http://registers.water.nsw.gov.au/wma/Glossary.jsp

11.\tAn additional worksheet was added to aggregate the data into Water Sources and Management Zones. The Water Sources and Management Zones were provided by NSW Office of Water

CombinedWSP_WSOURCES_31July2013.gdb\Geographic_GDA94\WSP_COMBINED_31July2013

12.\tThe Avon River does not have management zones. Therefore data can only be viewed for the water source.

13.\tAll other works can be aggregated to the Water Source, or the management zone depending on how you want to aggregate or disaggregate the data.

relevant fields:

CWLICENSE: works licence number

COUNT_CWLICENSE: Where there was more than one works per licence

SHARE_PER_WORKS: Share component divided by number of works to ensure no double counting

STATUS_DES: Status description as active, current, cancelled

LICENCE_iS: licensed issued date

LICENCE_LO: licence lodged date

LICENCE_P: Licence purpose eg. stock and domestic, town supply, irrigation

WORK_TYPE: pump, excavation etc

WORK_TYPE_: diversion or storage

MAJOR_CATC: major surface water catchement

NAME_OF_TH: water sharing plan the licence belongs to

WATER_SHAR: water sharing plan the licence belongs to

WATER_SOUR: water source

MANAGEMENT: management zone

WSP_STATUS: Status of the water sharing plan

START_DATE: Start date of the water sharing plan

END_DATE: end date of the water sharing plan

LICENSEorAPPROVAL: licence or approval number

Status: Cancelled or current (or blank)

ShareC: Share component attached to the licence

WAorCAlink:a combined water supply works / water use approval

LINKED_TO_AL:This is the identification number for an access licence which is shown on the licence certificate or on a search printout of the licence obtained from the access licence register run by Land and Property Information.

Dataset Citation

Bioregional Assessment Programme (XXXX) NSW Office of Water SW licences - Gloucester PAE v2 21022014. Bioregional Assessment Derived Dataset. Viewed 14 June 2016, http://data.bioregionalassessments.gov.au/dataset/f0a75a2b-233f-40a4-82cb-1929f2bee8c6.

Dataset Ancestors

• Derived From Subcatchment boundaries within and nearby the Gloucester subregion

• Derived From Bioregional Assessment areas v02

• Derived From Australian Coal Basins

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012.

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01

• Derived From Bioregional Assessment areas v01

• Derived From Geofabric Hydrology Reporting Catchments - V2.1

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From NSW Office of Water SurfaceWater licences in the Gloucester PAE

• Derived From GEODATA TOPO 250K Series 3

• Derived From Australian Geological Provinces, v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Gloucester Coal Basin

• Derived From Geological Provinces - Full Extent

• Derived From GLO Preliminary Assessment Extent

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

Alzheimer’s disease (AD) is the most common form of age-related dementia, and the most urgent problem is that it is currently incurable. Amyloid-β (Aβ) peptide is believed to play a major role in the pathogenesis of AD. We previously reported that an Aβ N-terminal amino acid targeting monoclonal antibody (MAb), A8, inhibits Aβ fibril formation and has potential as an immunotherapy for AD based on a mouse model. To further study the underlying mechanisms, we tested our hypothesis that the single chain fragment variable (scFv) without the Fc fragment is capable of regulating either Aβ aggregation or disaggregation in vitro. Here, a model of cell-free Aβ “on-pathway” aggregation was established and identified using PCR, Western blot, ELISA, transmission electron microscopy (TEM) and thioflavin T (ThT) binding analyses. His-tagged A8 scFvs was cloned and solubly expressed in baculovirus. Our data demonstrated that the Ni-NTA agarose affinity-purified A8 scFv inhibited the forward reaction of “on-pathway” aggregation and Aβ fibril maturation. The effect of A8 scFv on Aβ fibrillogenesis was markedly more significant when administered at the start of the Aβ folding reaction. Furthermore, the results also showed that pre-formed Aβ fibrils could be disaggregated via incubation with purified A8 scFv, which suggested that A8 scFv is involved in the reverse reaction of Aβ aggregation. Therefore, A8 scFv was capable of both inhibiting fibrillogenesis and disaggregating matured fibrils. Our present study provides valuable insight into the regulators of ultrastructural dynamics of cell-free “on-pathway” Aβ aggregation and will assist in the development of therapeutic strategies for AD.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Crop production values in this dataset are given per ha for each technology aggregated by categories - crops/food/non-food - with no information on individual crops. Unit of measure: Production per ha for each technology: mt/ha This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Harvested area values in this dataset are given for each technology aggregated by categories – crops/food/non-food - with no information on individual crops. Unit of measure: Harvested area for each technology: ha This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

See the Related Datasets section for access to the results of other disaggregation experiments conducted as part of this study.

These data were published in Song et al. (2023).
* This results publication includes additional supplementary material related to this dataset:
https://www.frontiersin.org/articles/10.3389/fmars.2023.1224518/full#supplementary-material

Red blood cells (RBCs) clump together under low flow conditions in a process called RBC aggregation, which can alter RBC perfusion in a microvascular network. As elevated RBC aggregation is commonly associated with cardiovascular and inflammatory diseases, a better understanding of aggregation is essential. Unlike RBC aggregation in polymer solutions which can be well explained by polymer depletion theory, plasma-mediated RBC aggregation has features that best match explanations with cross-bridging mechanisms. Previous studies have demonstrated the dominant role of fibrinogen (Fg) in promoting aggregate formation and recent cell-force spectroscopy (CFS) experiments on interacting RBC doublets in plasma have reported an inverse relationship between disaggregation force and the adhesive contact area between RBCs. This has led investigators to revisit the hypothesis of inter-RBC cross-bridging which involves cross-bridge migration under interfacial tension during the forced disaggregation of RBC aggregates. In this study, we developed the cross-bridge migration model (CBMM) in plasma that mechanistically represents the migrating cross-bridge hypothesis. Transport of mobile Fg cross-bridges (mFg) was calculated using a convection-diffusion transport equation with our novel introduction of convective cross-bridge drift that arises due to intercellular friction. By parametrically transforming the diffusivity of mFg in the CBMM, we were able to match experimental observations of both RBC doublet formation kinematics and RBC doublet disaggregation forces under optical tweezers tension. We found that non-specific cross-bridging promotes spontaneous growth of adhesion area between RBC doublets whereas specific cross-bridging tends to prevent adhesion area growth. Our CBMM was also able to correlate Fg concentration shifts from healthy population blood plasma to SLE (lupus) condition blood plasma with the observed increase in doublet disaggregation forces for the RBC doublets in SLE plasma.

Data for manuscript preprint on bioRxiv (https://doi.org/10.1101/2021.08.29.458036) and accepted for publication at EMBO Journal (https://doi.org/10.15252/embj.202111041).

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 …Show full descriptionAbstract 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. The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use. This has not been been clipped to North and South Sydney PAEs. Dataset History The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. Where purpose = domestic; or domestic & stock; or stock then it was classed as 'basic water right'. Where it is listed as both a domestic/stock and a licensed use such as irrigation, it was classed as a 'water access right.' All other take and use were classed as a 'water access right'. Dataset Citation Bioregional Assessment Programme (2014) NSW Office of Water SW Offtakes Processed - North & South Sydney, v3 12032014. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/7dc3a047-19a2-46ed-b519-b5e4f393aea1. Dataset Ancestors Derived From NSW Office of Water Surface Water Offtakes - North & South Sydney v1 24102013 Derived From NSW Office of Water SW Offtakes Processed - North & South Sydney, v2 07032014 Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

Modern slavery is a term that includes any form of human trafficking, slavery, servitude or forced labour, as set out in the Modern Slavery Act 2015. Potential victims of modern slavery in the UK that come to the attention of authorised ‘First Responder’ organisations are referred to the National Referral Mechanism (NRM).

Adults (aged 18 or above) must consent to being referred to the NRM, whilst children under the age of 18 need not consent to being referred. As specified in section 52 of the Modern Slavery Act 2015, public authorities in England and Wales have a statutory duty to notify the Home Office when they come across potential victims of modern slavery ('Duty to Notify' (DtN)). This duty is discharged by either referring a child or consenting adult potential victim into the NRM, or by notifying the Home Office via the DtN process if an adult victim does not consent to enter the NRM.

The Home Office publishes quarterly statistical bulletins and aggregated data breakdowns on the "https://www.gov.uk/government/collections/national-referral-mechanism-statistics" target="_blank"> National Referral Mechanism webpage on the GOV.UK website regarding the number of potential victims referred each quarter. To allow stakeholders and first responders more flexibility in analysing this data for their own strategic and operational planning, the disaggregated, pseudonymised dataset used to create the aggregated published data is also available from the UK Data Service as 'safeguarded' data. (The UKDS data are available in SPSS, Stata, tab-delimited text and CSV formats.)

Latest edition information

For the 18th edition (November 2025), the data file was amended to include Quarter 3 2025 cases, and the Data Notes documentation file was also updated.

The new scorecard tracks progress toward the World Bank Group's vision to create a world free of poverty on a livable planet. The Scorecard includes three types of indicators: - Vision indicators - reflect the new vision for the WBG, showing the WBG’s ambition and providing high-level measures to gauge the direction and pace of progress in tackling global challenges. Vision indicators contain aggregated and disaggregated development context data for all countries in the world, where data is available. The Scorecard reports the latest available global updates for each of these indicators. - Client context indicators - reflect the circumstances in client countries, including multidimensional aspects of poverty, and are aligned with the Sustainable Development Goals (SDGs). They serve to frame the challenges clients face, and the context in which the WBG operates. Client Context indicators contain aggregated and disaggregated development context data for World Bank client countries, based on country eligibility for financing and where data is available. The Scorecard also reports the latest available update for each of these indicators. - WBG Results indicators monitor WBG progress on some of the most critical global challenges. Results data include: - Active Portfolio Results: Contain achieved and expected results of WBG operations based on its active portfolio as of end of June 2024. Includes aggregated and disaggregated data. - Results achieved since July 1st, 2023: Contain cumulative results achieved between July 1st, 2023 - June 30, 2024 from active and closed projects. Results achieved before July 1st, 2023 are excluded from this calculation. Includes aggregated data for World Bank, IBRD and IDA only. IFC and MIGA do not currently report this data. - Operations Details: Operation-level detail is provided for World Bank projects. However, in alignment with IFC and MIGA Access to Information Policies, project-level data is available in an aggregated format on the WBG Scorecard, provided the minimum threshold to secure individual clients' data is satisfied. This collection includes only a subset of indicators from the source dataset.

Abstract: The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

Aggregation and disaggregation approaches compared in this example.

Each tab contains the data collected for each individual strain. The readme tab indicates the strains and plasmids used in Fig 7, as well as what values are contained in each column. (XLSX)