Attributing impacts of external climate drivers on extreme weather in Africa View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2013-2016

FUNDING AMOUNT

619259.0 GBP

ABSTRACT

Given limited progress in reducing greenhouse gas emissions and uncertain potential for adaptation to many impacts, attention in vulnerable regions and sectors is turning to the question of "loss and damage". Who should bear the costs of human influence on climate that cannot be neutralized by adaptation? This debate is impeded by lack of robust estimates of what these costs are. Despite concerted efforts to compile inventories of emissions, we still have no agreed method of establishing how countries, companies or individuals are being adversely affected by anthropogenic climate change in the context of other drivers of regional environmental change. Many of the most important impacts of climate change are related in some way to high-impact weather events (HIWEs), such as floods, storms, and droughts. Compiling an impact inventory requires documenting the impacts of individual events and how these events are affected by multiple climate drivers and internal climate variability. We will build on research into HIWEs and their impacts under THORPEX-Africa. Studies assessing the link between climate change and extreme weather have so far focused primarily on mid-latitude phenomena and the impact of rising greenhouse gases. Yet in many tropical regions, short-lived climate forcings (SLCFs) such as sulphate, mineral and black carbon aerosols and tropospheric ozone may have played a larger role in changing patterns of weather risk to date. Substantial reductions in anthropogenic SLCFs could be achieved in only 20 years. Including measures already planned to reduce emissions of sulphate aerosol precursors, SLCFs may dominate near-term changes in weather risk. Climate impact assessments used for adaptation planning typically focus on net multi-decadal anthropogenic change, dominated by greenhouse-induced warming. Few address uncertainty in SLCF forcing and response. Hence relying on these and extrapolation of recent trends risks "adapting to yesterday's problem" as key drivers of regional weather are reversed. Assessing the influence of external drivers on extreme weather is challenging because the most important events are typically rare. The only solution is to rely on simulation models, whose reliability can be tested and if necessary re-calibrated using well-established procedures developed for seasonal forecasting. We will also use the land-surface model JULES for indirect validation in regions with sparse meteorological data. Large ensembles of climate model simulations at relatively high resolution are required for robust statistics of extreme weather events, allowing for uncertainty in both external drivers and simulation models. This project makes use of the climateprediction.net weatherathome worldwide volunteer computing project. We will quantify the role of various external climate drivers on changing risks of extreme weather in Africa by implementing a regional climate model over the CORDEX-Africa domain and simulate observed weather statistics over recent decades using multi-thousand-member ensembles, systematically excluding the influence of different climate drivers to quantify their effects. Attribution studies of HIWEs to date have typically focussed on hydrometeorological events themselves, rather than modelling all the way through to their impacts. This can lead to "over-attribution": if a record-breaking weather event occurs that has been made more likely by some external driver, people tend to blame most of the impact of that event on that driver. But much of this impact might also have been caused by a lesser, non-record-breaking, event. Hence accurate assessment requires explicit modelling of changing impact risk, not simply weather risk, so a major focus of this project will using JULES to investigate various impacts and working with impact modellers across Africa to assess the implications of our weather simulations for changing impact risk in other sectors. More... »

URL

https://gtr.ukri.org/project/57102BF3-5C05-4DC4-91D4-4D2A1E1FDA01

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "amount": {
      "currency": "GBP", 
      "type": "MonetaryAmount", 
      "value": 619259.0
    }, 
    "description": "Given limited progress in reducing greenhouse gas emissions and uncertain potential for adaptation to many impacts, attention in vulnerable regions and sectors is turning to the question of \"loss and damage\". Who should bear the costs of human influence on climate that cannot be neutralized by adaptation? This debate is impeded by lack of robust estimates of what these costs are. Despite concerted efforts to compile inventories of emissions, we still have no agreed method of establishing how countries, companies or individuals are being adversely affected by anthropogenic climate change in the context of other drivers of regional environmental change.\n\nMany of the most important impacts of climate change are related in some way to high-impact weather events (HIWEs), such as floods, storms, and droughts. Compiling an impact inventory requires documenting the impacts of individual events and how these events are affected by multiple climate drivers and internal climate variability. We will build on research into HIWEs and their impacts under THORPEX-Africa.\n\nStudies assessing the link between climate change and extreme weather have so far focused primarily on mid-latitude phenomena and the impact of rising greenhouse gases. Yet in many tropical regions, short-lived climate forcings (SLCFs) such as sulphate, mineral and black carbon aerosols and tropospheric ozone may have played a larger role in changing patterns of weather risk to date. Substantial reductions in anthropogenic SLCFs could be achieved in only 20 years. Including measures already planned to reduce emissions of sulphate aerosol precursors, SLCFs may dominate near-term changes in weather risk. Climate impact assessments used for adaptation planning typically focus on net multi-decadal anthropogenic change, dominated by greenhouse-induced warming. Few address uncertainty in SLCF forcing and response. Hence relying on these and extrapolation of recent trends risks \"adapting to yesterday's problem\" as key drivers of regional weather are reversed.\n\nAssessing the influence of external drivers on extreme weather is challenging because the most important events are typically rare. The only solution is to rely on simulation models, whose reliability can be tested and if necessary re-calibrated using well-established procedures developed for seasonal forecasting. We will also use the land-surface model JULES for indirect validation in regions with sparse meteorological data. Large ensembles of climate model simulations at relatively high resolution are required for robust statistics of extreme weather events, allowing for uncertainty in both external drivers and simulation models.\n\nThis project makes use of the climateprediction.net weatherathome worldwide volunteer computing project. We will quantify the role of various external climate drivers on changing risks of extreme weather in Africa by implementing a regional climate model over the CORDEX-Africa domain and simulate observed weather statistics over recent decades using multi-thousand-member ensembles, systematically excluding the influence of different climate drivers to quantify their effects.\n\nAttribution studies of HIWEs to date have typically focussed on hydrometeorological events themselves, rather than modelling all the way through to their impacts. This can lead to \"over-attribution\": if a record-breaking weather event occurs that has been made more likely by some external driver, people tend to blame most of the impact of that event on that driver. But much of this impact might also have been caused by a lesser, non-record-breaking, event. Hence accurate assessment requires explicit modelling of changing impact risk, not simply weather risk, so a major focus of this project will using JULES to investigate various impacts and working with impact modellers across Africa to assess the implications of our weather simulations for changing impact risk in other sectors.", 
    "endDate": "2016-12-31", 
    "funder": {
      "id": "http://www.grid.ac/institutes/grid.8682.4", 
      "type": "Organization"
    }, 
    "id": "sg:grant.2761096", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "grant.2761096"
        ]
      }, 
      {
        "name": "gtr_id", 
        "type": "PropertyValue", 
        "value": [
          "57102BF3-5C05-4DC4-91D4-4D2A1E1FDA01"
        ]
      }
    ], 
    "inLanguage": [
      "en"
    ], 
    "keywords": [
      "high-impact weather events", 
      "external climate drivers", 
      "climate drivers", 
      "extreme weather", 
      "weather events", 
      "climate change", 
      "external drivers", 
      "land surface model JULES", 
      "CORDEX-Africa domain", 
      "internal climate variability", 
      "climate model simulations", 
      "regional climate model", 
      "greenhouse-induced warming", 
      "sulfate aerosol precursors", 
      "different climate drivers", 
      "sparse meteorological data", 
      "black carbon aerosols", 
      "regional environmental change", 
      "weather risk", 
      "climate impact assessments", 
      "near-term changes", 
      "anthropogenic climate change", 
      "multiple climate drivers", 
      "extreme weather events", 
      "climate forcings", 
      "climate models", 
      "seasonal forecasting", 
      "climate variability", 
      "regional weather", 
      "hydrometeorological events", 
      "carbon aerosols", 
      "impact modellers", 
      "tropospheric ozone", 
      "aerosol precursors", 
      "attribution studies", 
      "model simulations", 
      "weather simulations", 
      "weather statistics", 
      "meteorological data", 
      "human influence", 
      "anthropogenic changes", 
      "large ensemble", 
      "greenhouse gases", 
      "impact risk", 
      "individual events", 
      "vulnerable regions", 
      "environmental changes", 
      "tropical regions", 
      "weather", 
      "inventory of emissions", 
      "JULES", 
      "indirect validation", 
      "forcing", 
      "robust estimates", 
      "impact assessment", 
      "recent decades", 
      "events", 
      "high resolution", 
      "greenhouse gas emissions", 
      "gas emissions", 
      "Africa", 
      "address uncertainty", 
      "ensemble", 
      "important impact", 
      "storms", 
      "region", 
      "warming", 
      "SLCFs", 
      "minerals", 
      "floods", 
      "aerosols", 
      "climate", 
      "large role", 
      "key drivers", 
      "drivers", 
      "uncertainty", 
      "simulation model", 
      "drought", 
      "ozone", 
      "variability", 
      "forecasting", 
      "explicit modelling", 
      "emission", 
      "changes", 
      "SLCF", 
      "impact", 
      "gases", 
      "important events", 
      "Inventory", 
      "estimates", 
      "modelling", 
      "date", 
      "modelers", 
      "influence", 
      "model", 
      "simulations", 
      "robust statistics", 
      "sulfate", 
      "accurate assessment", 
      "resolution", 
      "extrapolation", 
      "project", 
      "sector", 
      "attribution", 
      "patterns", 
      "breaking", 
      "substantial reduction", 
      "decades", 
      "assessment", 
      "data", 
      "implications", 
      "statistics", 
      "years", 
      "validation", 
      "link", 
      "major focus", 
      "study", 
      "concerted effort", 
      "phenomenon", 
      "adaptation", 
      "lack", 
      "role", 
      "precursors", 
      "potential", 
      "only solution", 
      "response", 
      "loss", 
      "domain", 
      "reduction", 
      "debate", 
      "focus", 
      "efforts", 
      "context", 
      "effect", 
      "risk", 
      "progress", 
      "research", 
      "way", 
      "use", 
      "damage", 
      "method", 
      "questions", 
      "reliability", 
      "problem", 
      "attention", 
      "countries", 
      "measures", 
      "solution", 
      "limited progress", 
      "procedure", 
      "computing projects", 
      "cost", 
      "people", 
      "companies", 
      "individuals", 
      "attributing impact", 
      "impact inventory", 
      "uncertain potential", 
      "volunteer computing project", 
      "yesterday's problems"
    ], 
    "name": "Attributing impacts of external climate drivers on extreme weather in Africa", 
    "recipient": [
      {
        "id": "http://www.grid.ac/institutes/grid.4991.5", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.420255.4", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.266190.a", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.47840.3f", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.463088.1", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.17100.37", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.426193.b", 
        "type": "Organization"
      }, 
      {
        "id": "http://www.grid.ac/institutes/grid.437028.a", 
        "type": "Organization"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "Allen", 
        "givenName": "Myles", 
        "id": "sg:person.0600474550.17", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.0600474550.17", 
        "roleName": "PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "BOWERY", 
        "givenName": "ANDREW", 
        "id": "sg:person.012745202433.04", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.012745202433.04", 
        "roleName": "Co-PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "Dadson", 
        "givenName": "Simon", 
        "id": "sg:person.0604221520.18", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.0604221520.18", 
        "roleName": "Co-PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "Otto", 
        "givenName": "Friederike", 
        "id": "sg:person.01326463237.80", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.01326463237.80", 
        "roleName": "Co-PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "Wallom", 
        "givenName": "David", 
        "id": "sg:person.015131374022.11", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.015131374022.11", 
        "roleName": "Co-PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "http://www.grid.ac/institutes/None", 
          "name": "University of Oxford", 
          "type": "Organization"
        }, 
        "familyName": "Willis", 
        "givenName": "Katherine", 
        "id": "sg:person.01313107210.71", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.01313107210.71", 
        "roleName": "Co-PI", 
        "type": "Role"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.2761096"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2022-01-01T19:31", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/grant/grant_60.jsonl", 
    "startDate": "2013-06-23", 
    "type": "MonetaryGrant", 
    "url": "https://gtr.ukri.org/project/57102BF3-5C05-4DC4-91D4-4D2A1E1FDA01"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.2761096'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.2761096'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.2761096'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.2761096'


 

This table displays all metadata directly associated to this object as RDF triples.

264 TRIPLES      19 PREDICATES      199 URIs      179 LITERALS      10 BLANK NODES

Subject Predicate Object
1 sg:grant.2761096 schema:about anzsrc-for:04
2 schema:amount Nd494ffb70bea40e588d57ccbfe10eef0
3 schema:description Given limited progress in reducing greenhouse gas emissions and uncertain potential for adaptation to many impacts, attention in vulnerable regions and sectors is turning to the question of "loss and damage". Who should bear the costs of human influence on climate that cannot be neutralized by adaptation? This debate is impeded by lack of robust estimates of what these costs are. Despite concerted efforts to compile inventories of emissions, we still have no agreed method of establishing how countries, companies or individuals are being adversely affected by anthropogenic climate change in the context of other drivers of regional environmental change. Many of the most important impacts of climate change are related in some way to high-impact weather events (HIWEs), such as floods, storms, and droughts. Compiling an impact inventory requires documenting the impacts of individual events and how these events are affected by multiple climate drivers and internal climate variability. We will build on research into HIWEs and their impacts under THORPEX-Africa. Studies assessing the link between climate change and extreme weather have so far focused primarily on mid-latitude phenomena and the impact of rising greenhouse gases. Yet in many tropical regions, short-lived climate forcings (SLCFs) such as sulphate, mineral and black carbon aerosols and tropospheric ozone may have played a larger role in changing patterns of weather risk to date. Substantial reductions in anthropogenic SLCFs could be achieved in only 20 years. Including measures already planned to reduce emissions of sulphate aerosol precursors, SLCFs may dominate near-term changes in weather risk. Climate impact assessments used for adaptation planning typically focus on net multi-decadal anthropogenic change, dominated by greenhouse-induced warming. Few address uncertainty in SLCF forcing and response. Hence relying on these and extrapolation of recent trends risks "adapting to yesterday's problem" as key drivers of regional weather are reversed. Assessing the influence of external drivers on extreme weather is challenging because the most important events are typically rare. The only solution is to rely on simulation models, whose reliability can be tested and if necessary re-calibrated using well-established procedures developed for seasonal forecasting. We will also use the land-surface model JULES for indirect validation in regions with sparse meteorological data. Large ensembles of climate model simulations at relatively high resolution are required for robust statistics of extreme weather events, allowing for uncertainty in both external drivers and simulation models. This project makes use of the climateprediction.net weatherathome worldwide volunteer computing project. We will quantify the role of various external climate drivers on changing risks of extreme weather in Africa by implementing a regional climate model over the CORDEX-Africa domain and simulate observed weather statistics over recent decades using multi-thousand-member ensembles, systematically excluding the influence of different climate drivers to quantify their effects. Attribution studies of HIWEs to date have typically focussed on hydrometeorological events themselves, rather than modelling all the way through to their impacts. This can lead to "over-attribution": if a record-breaking weather event occurs that has been made more likely by some external driver, people tend to blame most of the impact of that event on that driver. But much of this impact might also have been caused by a lesser, non-record-breaking, event. Hence accurate assessment requires explicit modelling of changing impact risk, not simply weather risk, so a major focus of this project will using JULES to investigate various impacts and working with impact modellers across Africa to assess the implications of our weather simulations for changing impact risk in other sectors.
4 schema:endDate 2016-12-31
5 schema:funder grid-institutes:grid.8682.4
6 schema:identifier N380389b6af37431b8b22c195eb7488dc
7 N9335ba66312044cf970c06cd9f403273
8 schema:inLanguage en
9 schema:keywords Africa
10 CORDEX-Africa domain
11 Inventory
12 JULES
13 SLCF
14 SLCFs
15 accurate assessment
16 adaptation
17 address uncertainty
18 aerosol precursors
19 aerosols
20 anthropogenic changes
21 anthropogenic climate change
22 assessment
23 attention
24 attributing impact
25 attribution
26 attribution studies
27 black carbon aerosols
28 breaking
29 carbon aerosols
30 changes
31 climate
32 climate change
33 climate drivers
34 climate forcings
35 climate impact assessments
36 climate model simulations
37 climate models
38 climate variability
39 companies
40 computing projects
41 concerted effort
42 context
43 cost
44 countries
45 damage
46 data
47 date
48 debate
49 decades
50 different climate drivers
51 domain
52 drivers
53 drought
54 effect
55 efforts
56 emission
57 ensemble
58 environmental changes
59 estimates
60 events
61 explicit modelling
62 external climate drivers
63 external drivers
64 extrapolation
65 extreme weather
66 extreme weather events
67 floods
68 focus
69 forcing
70 forecasting
71 gas emissions
72 gases
73 greenhouse gas emissions
74 greenhouse gases
75 greenhouse-induced warming
76 high resolution
77 high-impact weather events
78 human influence
79 hydrometeorological events
80 impact
81 impact assessment
82 impact inventory
83 impact modellers
84 impact risk
85 implications
86 important events
87 important impact
88 indirect validation
89 individual events
90 individuals
91 influence
92 internal climate variability
93 inventory of emissions
94 key drivers
95 lack
96 land surface model JULES
97 large ensemble
98 large role
99 limited progress
100 link
101 loss
102 major focus
103 measures
104 meteorological data
105 method
106 minerals
107 model
108 model simulations
109 modelers
110 modelling
111 multiple climate drivers
112 near-term changes
113 only solution
114 ozone
115 patterns
116 people
117 phenomenon
118 potential
119 precursors
120 problem
121 procedure
122 progress
123 project
124 questions
125 recent decades
126 reduction
127 region
128 regional climate model
129 regional environmental change
130 regional weather
131 reliability
132 research
133 resolution
134 response
135 risk
136 robust estimates
137 robust statistics
138 role
139 seasonal forecasting
140 sector
141 simulation model
142 simulations
143 solution
144 sparse meteorological data
145 statistics
146 storms
147 study
148 substantial reduction
149 sulfate
150 sulfate aerosol precursors
151 tropical regions
152 tropospheric ozone
153 uncertain potential
154 uncertainty
155 use
156 validation
157 variability
158 volunteer computing project
159 vulnerable regions
160 warming
161 way
162 weather
163 weather events
164 weather risk
165 weather simulations
166 weather statistics
167 years
168 yesterday's problems
169 schema:name Attributing impacts of external climate drivers on extreme weather in Africa
170 schema:recipient N0f10ccbee01549c489a40f277b542893
171 N334c9bb987a443da92ff3c0f7d9f4618
172 N61a94aa263ad4af78773be01c5761a71
173 N7981b034600b462792fa807b4830b551
174 Nd0b0adfa2c544449968a2a4cc1a6f9cb
175 Nefd6cc1060654b13931983c5f902ec25
176 sg:person.012745202433.04
177 sg:person.01313107210.71
178 sg:person.01326463237.80
179 sg:person.015131374022.11
180 sg:person.0600474550.17
181 sg:person.0604221520.18
182 grid-institutes:grid.17100.37
183 grid-institutes:grid.266190.a
184 grid-institutes:grid.420255.4
185 grid-institutes:grid.426193.b
186 grid-institutes:grid.437028.a
187 grid-institutes:grid.463088.1
188 grid-institutes:grid.47840.3f
189 grid-institutes:grid.4991.5
190 schema:sameAs https://app.dimensions.ai/details/grant/grant.2761096
191 schema:sdDatePublished 2022-01-01T19:31
192 schema:sdLicense https://scigraph.springernature.com/explorer/license/
193 schema:sdPublisher Nb126d26bf0d542f0b88f0a9208df5ab0
194 schema:startDate 2013-06-23
195 schema:url https://gtr.ukri.org/project/57102BF3-5C05-4DC4-91D4-4D2A1E1FDA01
196 sgo:license sg:explorer/license/
197 sgo:sdDataset grants
198 rdf:type schema:MonetaryGrant
199 N0f10ccbee01549c489a40f277b542893 schema:member sg:person.015131374022.11
200 schema:roleName Co-PI
201 rdf:type schema:Role
202 N334c9bb987a443da92ff3c0f7d9f4618 schema:member sg:person.0600474550.17
203 schema:roleName PI
204 rdf:type schema:Role
205 N380389b6af37431b8b22c195eb7488dc schema:name dimensions_id
206 schema:value grant.2761096
207 rdf:type schema:PropertyValue
208 N61a94aa263ad4af78773be01c5761a71 schema:member sg:person.0604221520.18
209 schema:roleName Co-PI
210 rdf:type schema:Role
211 N7981b034600b462792fa807b4830b551 schema:member sg:person.01326463237.80
212 schema:roleName Co-PI
213 rdf:type schema:Role
214 N9335ba66312044cf970c06cd9f403273 schema:name gtr_id
215 schema:value 57102BF3-5C05-4DC4-91D4-4D2A1E1FDA01
216 rdf:type schema:PropertyValue
217 Nb126d26bf0d542f0b88f0a9208df5ab0 schema:name Springer Nature - SN SciGraph project
218 rdf:type schema:Organization
219 Nd0b0adfa2c544449968a2a4cc1a6f9cb schema:member sg:person.01313107210.71
220 schema:roleName Co-PI
221 rdf:type schema:Role
222 Nd494ffb70bea40e588d57ccbfe10eef0 schema:currency GBP
223 schema:value 619259.0
224 rdf:type schema:MonetaryAmount
225 Nefd6cc1060654b13931983c5f902ec25 schema:member sg:person.012745202433.04
226 schema:roleName Co-PI
227 rdf:type schema:Role
228 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
229 rdf:type schema:DefinedTerm
230 sg:person.012745202433.04 schema:affiliation grid-institutes:None
231 schema:familyName BOWERY
232 schema:givenName ANDREW
233 rdf:type schema:Person
234 sg:person.01313107210.71 schema:affiliation grid-institutes:None
235 schema:familyName Willis
236 schema:givenName Katherine
237 rdf:type schema:Person
238 sg:person.01326463237.80 schema:affiliation grid-institutes:None
239 schema:familyName Otto
240 schema:givenName Friederike
241 rdf:type schema:Person
242 sg:person.015131374022.11 schema:affiliation grid-institutes:None
243 schema:familyName Wallom
244 schema:givenName David
245 rdf:type schema:Person
246 sg:person.0600474550.17 schema:affiliation grid-institutes:None
247 schema:familyName Allen
248 schema:givenName Myles
249 rdf:type schema:Person
250 sg:person.0604221520.18 schema:affiliation grid-institutes:None
251 schema:familyName Dadson
252 schema:givenName Simon
253 rdf:type schema:Person
254 grid-institutes:None schema:name University of Oxford
255 rdf:type schema:Organization
256 grid-institutes:grid.17100.37 schema:Organization
257 grid-institutes:grid.266190.a schema:Organization
258 grid-institutes:grid.420255.4 schema:Organization
259 grid-institutes:grid.426193.b schema:Organization
260 grid-institutes:grid.437028.a schema:Organization
261 grid-institutes:grid.463088.1 schema:Organization
262 grid-institutes:grid.47840.3f schema:Organization
263 grid-institutes:grid.4991.5 schema:Organization
264 grid-institutes:grid.8682.4 schema:Organization
 




Preview window. Press ESC to close (or click here)


...