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### Instruction: Extract the cause and effect entities from the following sentence: Exact individual metadata will be iterated with the ILSTE-WG to ensure all metadata requirements are covered ### Assistant: VARIABLE: all metadata requirements CONDITION: are covered CAUSE_1: Exact individual metadata will be iterated with the ILSTE-WG KEYWORD: to ensure EFFECT_1: all metadata requirements are covered ### END
### Instruction: Extract the cause and effect entities from the following sentence: For high latitude applications then equal area projections are more suitable and as such dedicated polar IST / LST datasets will be available in these projections ### Assistant: KEYWORD: then CONDITION: will be available in these projections VARIABLE: as such dedicated polar IST / LST datasets AND: and EFFECT_2: as such dedicated polar IST / LST datasets will be available in these projections EFFECT_1: equal area projections are more suitable CAUSE_1: high latitude applications ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where data adjustment is performed this will be provided in the harmonised format as attribute comments ### Assistant: KEYWORD: Where VARIABLE: this CONDITION: will be provided in the harmonised format as attribute comments CAUSE_1: data adjustment is performed EFFECT_1: this will be provided in the harmonised format as attribute comments ### END
### Instruction: Extract the cause and effect entities from the following sentence: A repository will be set up to allow users to share data reader and visualisation tools within the community ### Assistant: VARIABLE: users CONDITION: visualisation tools within the community EFFECT_1: to allow users to share data reader AND: and CAUSE_1: A repository will be set up EFFECT_2: visualisation tools within the community ### END
### Instruction: Extract the cause and effect entities from the following sentence: Since this requires data from multiple input data streams with considerable processing requirements such timeliness is not considered feasible ### Assistant: KEYWORD: Since VARIABLE: such timeliness CONDITION: considered feasible NEGATION: is not EFFECT_1: such timeliness is not considered feasible CAUSE_1: this requires data from multiple input data streams with considerable processing requirements ### END
### Instruction: Extract the cause and effect entities from the following sentence: Continuation of this data will be sustained with the launch of the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel-3 ### Assistant: VARIABLE: Land Surface Temperature Radiometer (SLSTR) on board Sentinel-3 CONDITION: the launch of KEYWORD: with AND: and EFFECT_1: Continuation of this data will be sustained CAUSE_1: the launch of the Sea CAUSE_2: Land Surface Temperature Radiometer (SLSTR) on board Sentinel-3 ### END
### Instruction: Extract the cause and effect entities from the following sentence: With a swath width of 512km, AATSR is able to provide approximately 3-day global LST coverage with a repeat cycle of 35 days ### Assistant: KEYWORD: With VARIABLE: a repeat cycle CONDITION: of 35 days CAUSE_1: a swath width of 512km AND: with EFFECT_1: AATSR is able to provide approximately 3-day global LST coverage EFFECT_2: a repeat cycle of 35 days ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where snow or ice is identified the pixel is reclassified as permanent ice (ALB-2 class 27) and LST is retrieved by applying the associated coefficients ### Assistant: KEYWORD: Where VARIABLE: LST OR: or CONDITION: is retrieved by applying the associated coefficients NOT_RELEVANT: (ALB-2 class 27) AND: and CAUSE_1: snow CAUSE_2: ice is identified EFFECT_1: the pixel is reclassified as permanent ice EFFECT_2: LST is retrieved by applying the associated coefficients ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where a surface is identified as transient snow/ice or sea-ice these IST coefficients are applied instead of the respective LST or SST coefficients ### Assistant: KEYWORD: Where VARIABLE: SST coefficients CONDITION: are applied OR: or NEGATION: instead CAUSE_1: a surface is identified as transient snow/ice CAUSE_2: sea-ice EFFECT_1: these IST coefficients are applied EFFECT_2: of the respective LST EFFECT_3: SST coefficients ### END
### Instruction: Extract the cause and effect entities from the following sentence: Although AVHRR/3 is a six-channel radiometer, only five channels are transmitted to the ground at any given time, since channels 3a and 3b do not operate simultaneously ### Assistant: NOT_RELEVANT: Although AVHRR/3 is a six-channel radiometer KEYWORD: since VARIABLE: 3b CONDITION: operate simultaneously AND: and NEGATION: do not CAUSE_2: 3b do not operate simultaneously CAUSE_1: channels 3a EFFECT_1: five channels are transmitted to the ground at any given time ### END
### Instruction: Extract the cause and effect entities from the following sentence: For Metop satellites, channel 3a is operated during the daytime portion of the orbit and channel 3b during the night-time portion ### Assistant: KEYWORD: during VARIABLE: the night-time portion CAUSE_1: Metop satellites CONDITION: is operated EFFECT_1: channel 3a is operated CAUSE_2: the daytime portion of the orbit AND: and CAUSE_3: the night-time portion EFFECT_2: channel 3b ### END
### Instruction: Extract the cause and effect entities from the following sentence: The nominal SSP is located at 0º longitude and therefore the MSG disk covers Africa, most of Europe and part of South America ### Assistant: CONDITION: part of South America VARIABLE: the MSG disk CAUSE_1: The nominal SSP is located at 0º longitude KEYWORD: therefore EFFECT_1: the MSG disk covers Africa AND: and EFFECT_2: most of Europe EFFECT_3: part of South America ### END
### Instruction: Extract the cause and effect entities from the following sentence: VIIRS controls pixel growth towards the edge of scan such that the pixel sizes are comparable to nadir ### Assistant: KEYWORD: such that VARIABLE: VIIRS CONDITION: controls pixel growth towards the edge of scan EFFECT_1: the pixel sizes are comparable to nadir CAUSE_1: VIIRS controls pixel growth towards the edge of scan ### END
### Instruction: Extract the cause and effect entities from the following sentence: On the ground, the EFOV is 50 km by 50 km at nadir, and the distance between two EFOV is approximately 50 km ### Assistant: VARIABLE: the distance between two EFOV CAUSE_1: the ground EFFECT_1: the EFOV is 50 km by 50 km at nadir AND: and CONDITION: is approximately 50 km EFFECT_2: the distance between two EFOV is approximately 50 km KEYWORD: On ### END
### Instruction: Extract the cause and effect entities from the following sentence: Since non- GlobTemperature funded international collaborators may be unlikely to be able to provide datasets in the agreed GlobTemperature harmonised format, the Data Portal will not restrict access to only datasets in harmonised formats ### Assistant: KEYWORD: Since VARIABLE: the Data Portal NEGATION: will not CONDITION: restrict access to only datasets in harmonised formats CAUSE_1: non- GlobTemperature funded international collaborators may be unlikely to be able to provide datasets in the agreed GlobTemperature harmonised format EFFECT_1: the Data Portal will not restrict access to only datasets in harmonised formats ### END
### Instruction: Extract the cause and effect entities from the following sentence: For any lake pixels where Arc-Lake is not available, the UOL_LST_2P retrievals for the inland water biome are to be used ### Assistant: VARIABLE: Arc-Lake KEYWORD: where CONDITION: are to be used CAUSE_1: any lake pixels EFFECT_1: the UOL_LST_2P retrievals for the inland water biome are to be used NEGATION: is not CAUSE_2: Arc-Lake is not available ### END
### Instruction: Extract the cause and effect entities from the following sentence: The locations where Arc-Lake retrievals are derived are defined using the ARC-Lake land/water mask ### Assistant: VARIABLE: Arc-Lake retrievals CONDITION: are defined using the ARC-Lake land/water mask. CAUSE_1: Arc-Lake retrievals are derived EFFECT_1: The locations KEYWORD: where ### END
### Instruction: Extract the cause and effect entities from the following sentence: At each new LSWT release of GloboLakes the (A)ATSR GlobTemperature single-sensor dataset will be re-processed ### Assistant: KEYWORD: At each CONDITION: will be re-processed VARIABLE: the (A)ATSR GlobTemperature single-sensor dataset CAUSE_1: new LSWT release of GloboLakes EFFECT_1: the (A)ATSR GlobTemperature single-sensor dataset will be re-processed ### END
### Instruction: Extract the cause and effect entities from the following sentence: Once the GEO data are merged, LEO data and GEO data can be merged; in areas not covered by GEO observations the following stage of the merging process is based on LEO-only data ### Assistant: KEYWORD: in VARIABLE: the following stage of the merging process CONDITION: is based on LEO-only data CAUSE_1: the GEO data are merged AND: and EFFECT_1: LEO data EFFECT_2: GEO data can be merged NEGATION: not CAUSE_2: areas not covered by GEO observations EFFECT_3: the following stage of the merging process is based on LEO-only data ### END
### Instruction: Extract the cause and effect entities from the following sentence: All orbits overpassing a given site will be extracted as subsets for matching in the GT_MDB ### Assistant: VARIABLE: All orbits CONDITION: will be extracted as subsets for matching in the GT_MDB CAUSE_1: All orbits overpassing a given site EFFECT_1: will be extracted as subsets for matching in the GT_MDB ### END
### Instruction: Extract the cause and effect entities from the following sentence: Once the protocol is finalised then the use of open source solutions for data extraction will also be considered (a recommended solution is GDAL (www.gdal.org)) ### Assistant: KEYWORD: then VARIABLE: the use of open source solutions for data extraction CONDITION: will also be considered CAUSE_1: the protocol is finalised EFFECT_1: the use of open source solutions for data extraction will also be considered NOT_RELEVANT: (a recommended solution is GDAL (www.gdal.org)) ### END
### Instruction: Extract the cause and effect entities from the following sentence: In-situ emissivities need to be determined with appropriate spatial and temporal sampling to be representative of LSE ### Assistant: VARIABLE: In-situ emissivities CONDITION: temporal sampling AND: and CAUSE_2: temporal sampling CAUSE_1: In-situ emissivities need to be determined with appropriate spatial EFFECT_1: to be representative of LSE ### END
### Instruction: Extract the cause and effect entities from the following sentence: However, in case of changes in surface moisture LSE may need to be retrieved several times per day ### Assistant: KEYWORD: in case of VARIABLE: LSE CAUSE_1: changes in surface moisture CONDITION: may need to be retrieved several times per day. EFFECT_1: LSE may need to be retrieved several times per day NOT_RELEVANT: However ### END
### Instruction: Extract the cause and effect entities from the following sentence: Depending on the surface type, the accuracy of in-situ LSE is usually between ±0.005 and ±0.015 ### Assistant: KEYWORD: Depending on VARIABLE: the accuracy of in-situ LSE CAUSE_1: the surface type CONDITION: is usually between ±0.005 and ±0.015 EFFECT_1: the accuracy of in-situ LSE is usually between ±0.005 and ±0.015. ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where no in-situ emissivity data are available, the use of in-situ emissivities derived from MOD21 LSE is recommended ### Assistant: KEYWORD: Where NEGATION: no VARIABLE: the use of in-situ emissivities CONDITION: derived from MOD21 LSE CAUSE_1: no in-situ emissivity data are available EFFECT_1: the use of in-situ emissivities CAUSE_2: derived from MOD21 LSE ### END
### Instruction: Extract the cause and effect entities from the following sentence: Radiance-based validation offers a useful alternative to validation with in situ LST as it does not require in-situ measurements concurrent with the satellite overpass [RD-67] ### Assistant: VARIABLE: it CONDITION: require in-situ measurements concurrent with the satellite overpass EFFECT_1: Radiance-based validation offers a useful alternative to validation with in situ LST KEYWORD: as NEGATION: does not NOT_RELEVANT: [RD-67] CAUSE_1: it does not require in-situ measurements concurrent with the satellite overpass ### END
### Instruction: Extract the cause and effect entities from the following sentence: For selecting locations where R-based validation is to be carried out, a key criterion is the spatial homogeneity and temporal stability of the surface in terms of emissivity ### Assistant: KEYWORD: where CONDITION: temporal stability of the surface in terms of emissivity VARIABLE: a key criterion AND: and CAUSE_1: selecting locations CAUSE_2: R-based validation is to be carried out EFFECT_1: a key criterion is the spatial homogeneity EFFECT_2: temporal stability of the surface in terms of emissivity ### END
### Instruction: Extract the cause and effect entities from the following sentence: When the result of this ‘double-differencing test’, which was first implemented by [RD-67], is close to zero, then the atmospheric profiles can be assumed to be representative of the real conditions ### Assistant: KEYWORD: then VARIABLE: the atmospheric profiles NOT_RELEVANT: which was first implemented by [RD-67] CONDITION: can be assumed to be representative of the real conditions EFFECT_1: the atmospheric profiles can be assumed to be representative of the real conditions CAUSE_1: is close to zero ### END
### Instruction: Extract the cause and effect entities from the following sentence: Intercomparisons are also mandatory when combining several products into a ‘best available product’ ### Assistant: VARIABLE: several products CONDITION: into a ‘best available product’ KEYWORD: when EFFECT_1: Intercomparisons are also mandatory CAUSE_1: combining several products into a ‘best available product’ ### END
### Instruction: Extract the cause and effect entities from the following sentence: However, product intercomparisons on their own are not sufficient for comprehensive validation since the various products could be ‘consistent’ but nonetheless biased with respect to reference data, i.e. in-situ LST ### Assistant: NOT_RELEVANT: i.e. in-situ LST VARIABLE: the various products NEGATION: are not CONDITION: nonetheless biased with respect to reference data KEYWORD: since AND: but EFFECT_1: product intercomparisons on their own are not sufficient for comprehensive validation CAUSE_1: the various products could be ‘consistent’ CAUSE_2: nonetheless biased with respect to reference data ### END
### Instruction: Extract the cause and effect entities from the following sentence: This step is very important when analysing long time series or comparing different regions ### Assistant: VARIABLE: different regions CONDITION: comparing KEYWORD: when OR: or CAUSE_1: analysing long time series EFFECT_1: This step is very important CAUSE_2: comparing different regions ### END
### Instruction: Extract the cause and effect entities from the following sentence: In case such surfaces are not available, e.g. if there is a heterogeneous and dynamic vegetation cover in the target area, precision may also be quantified by evaluating the ‘smoothness’ of the time series of some geostatistical metric, g. variograms generally show significant variability (nugget) for pixels separated by very short distances ### Assistant: KEYWORD: In case VARIABLE: precision CONDITION: may also be quantified by evaluating the ‘smoothness’ of the time series of some geostatistical metric CAUSE_1: such surfaces are not available NOT_RELEVANT: g. variograms generally show significant variability (nugget) for pixels separated by very short distances. EFFECT_1: precision may also be quantified by evaluating the ‘smoothness’ of the time series of some geostatistical metric NEGATION: are not ### END
### Instruction: Extract the cause and effect entities from the following sentence: For each subsequent class, one of the listed criteria can be relaxed ### Assistant: KEYWORD: For CONDITION: can be relaxed VARIABLE: each CAUSE_1: each subsequent class EFFECT_1: one of the listed criteria can be relaxed. ### END
### Instruction: Extract the cause and effect entities from the following sentence: In order to increase the number of primary in-situ validation sites for the purpose of GlobTemperature, additional sites were selected based on a) their long-term operational availability and their category according to [RD-5] ### Assistant: KEYWORD: In order to VARIABLE: additional sites CONDITION: their category according to [RD-5] EFFECT_1: to increase the number of primary in-situ validation sites NOT_RELEVANT: for the purpose of GlobTemperature CAUSE_1: additional sites were selected based on a) their long-term operational availability AND: and CAUSE_2: their category according to [RD-5] ### END
### Instruction: Extract the cause and effect entities from the following sentence: For radiometers measuring in the atmospheric window and for a small distance between the sensor and the surface (a few 10m), atmospheric attenuation of the surface-leaving IR radiance can be neglected ### Assistant: KEYWORD: for VARIABLE: a CONDITION: small AND: and NOT_RELEVANT: (a few 10m) EFFECT_1: atmospheric attenuation of the surface-leaving IR radiance can be neglected CAUSE_1: radiometers measuring in the atmospheric window CAUSE_2: a small distance between the sensor and the surface ### END
### Instruction: Extract the cause and effect entities from the following sentence: Depending on surface emissivity and on downwelling longwave radiance (e.g. a cold clear sky vs. a warm humid atmosphere), the reflection term in the above equation can cause differences of several degrees Celsius [RD-84] ### Assistant: KEYWORD: on VARIABLE: the reflection term in the above equation AND: and CONDITION: can cause differences of several degrees Celsius NOT_RELEVANT: [RD-84] CAUSE_1: surface emissivity CAUSE_2: downwelling longwave radiance EFFECT_1: the reflection term in the above equation can cause differences of several degrees Celsius ### END
### Instruction: Extract the cause and effect entities from the following sentence: In order to avoid influence of the atmosphere on the broadband measurements (3-50µm), the distance between the in-situ sensor and the target has to be small, since the sensors also measure outside the atmospheric window ### Assistant: KEYWORD: since CONDITION: also measure outside the atmospheric window VARIABLE: the sensors EFFECT_1: to avoid influence of the atmosphere on the broadband measurements NOT_RELEVANT: (3-50µm) CAUSE_1: the distance between the in-situ sensor and the target has to be small CAUSE_2: the sensors also measure outside the atmospheric window ### END
### Instruction: Extract the cause and effect entities from the following sentence: Only feasible matchup combinations are listed; where the orbit characteristics of two sensors are not compatible for matching LST then no entry is made ### Assistant: KEYWORD: Only VARIABLE: matchup combinations CONDITION: are listed NEGATION: are not CAUSE_2: the orbit characteristics of two sensors are not compatible for matching LST EFFECT_2: entry is made CAUSE_1: feasible matchup combinations EFFECT_1: are listed ### END
### Instruction: Extract the cause and effect entities from the following sentence: For each data set, the user will be able to consult a detailed version of the metadata ### Assistant: KEYWORD: For VARIABLE: the user CAUSE_1: each data set CONDITION: will be able to consult a detailed version of the metadata EFFECT_1: the user will be able to consult a detailed version of the metadata ### END
### Instruction: Extract the cause and effect entities from the following sentence: For each order, the link between the registered user and the associated order will be stored in the database ### Assistant: KEYWORD: For VARIABLE: the link between the registered user and the associated order CONDITION: will be stored in the database CAUSE_1: each order EFFECT_1: the link between the registered user and the associated order will be stored in the database ### END
### Instruction: Extract the cause and effect entities from the following sentence: In response to the order received, the data is transferred from the archive to the Download area ### Assistant: KEYWORD: In response to VARIABLE: the data CONDITION: is transferred from the archive to the Download area CAUSE_1: the order received EFFECT_1: the data is transferred from the archive to the Download area ### END
### Instruction: Extract the cause and effect entities from the following sentence: A script will be set up on the dissemination system to automatically delete any ordered products older than 15 days ### Assistant: VARIABLE: ordered products NOT_RELEVANT: will be set up on the dissemination system CONDITION: to automatically delete CAUSE_1: ordered products older than 15 days KEYWORD: any EFFECT_1: to automatically delete ### END
### Instruction: Extract the cause and effect entities from the following sentence: Additional detailed specifications will be appended in further versions in response to the users need and to recommendations of the International LST & E Working Group ### Assistant: KEYWORD: in response to VARIABLE: the International LST & E Working Group AND: and CONDITION: will be appended in further versions CAUSE_1: the users need CAUSE_2: to recommendations of the International LST & E Working Group EFFECT_1: Additional detailed specifications will be appended in further versions ### END
### Instruction: Extract the cause and effect entities from the following sentence: The presence of the cloud bits within the QC flags enable easy filtering of cloud from the delivered data ### Assistant: CONDITION: enable VARIABLE: easy filtering of cloud from the delivered data EFFECT_1: enable easy filtering of cloud from the delivered data CAUSE_1: The presence of the cloud bits within the QC flags ### END
### Instruction: Extract the cause and effect entities from the following sentence: To facilitate optimum user uptake of the GlobTemperature products in harmonised format a set of tools will be developed in Python, which is open-source and platform independent software for reading and sub-setting the datafiles created in the harmonised formats ### Assistant: CONDITION: will be developed in Python VARIABLE: a set of tools EFFECT_1: To facilitate optimum user uptake of the GlobTemperature products in harmonised format CAUSE_1: a set of tools will be developed in Python NOT_RELEVANT: which is open-source and platform independent software for reading and sub-setting the datafiles created in the harmonised formats. ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where emissivity data are explicit these can be provided in the AUX datafiles ### Assistant: KEYWORD: Where VARIABLE: emissivity data CONDITION: these can be provided in the AUX datafiles CAUSE_1: emissivity data are explicit EFFECT_1: these can be provided in the AUX datafiles ### END
### Instruction: Extract the cause and effect entities from the following sentence: The daily IST fields will be available in Netcdf format to other partners within the GlobTemperature project after generation ### Assistant: NEGATION: after VARIABLE: The daily IST fields CAUSE_1: generation CONDITION: will be available in Netcdf format to other partners within the GlobTemperature project EFFECT_1: The daily IST fields will be available in Netcdf format to other partners within the GlobTemperature project ### END
### Instruction: Extract the cause and effect entities from the following sentence: Consequently, the need for trustworthy forecasts of atmospheric and oceanographic conditions is highly needed for safely marine operations in ice-covered seas ### Assistant: KEYWORD: Consequently CONDITION: is highly needed VARIABLE: oceanographic conditions CAUSE_1: safely marine operations in ice-covered seas AND: and EFFECT_1: the need for trustworthy forecasts of atmospheric EFFECT_2: oceanographic conditions is highly needed ### END
### Instruction: Extract the cause and effect entities from the following sentence: Once the triangles are calculated, results will be checked to remove those dates where the LST-VI triangle is not properly defined ### Assistant: KEYWORD: where VARIABLE: the LST-VI triangle CONDITION: properly defined CAUSE_1: the triangles are calculated EFFECT_1: results will be checked to remove those dates NEGATION: is not CAUSE_2: the LST-VI triangle is not properly defined ### END
### Instruction: Extract the cause and effect entities from the following sentence: Because of the recent improvements to both the modelling of the land surface and assimilation at the Met Office the source of the satellite data will need to be from a recent observing period ### Assistant: KEYWORD: Because of VARIABLE: the modelling of the land surface AND: both CAUSE_1: the modelling of the land surface CAUSE_2: assimilation at the Met Office CONDITION: will need to be from a recent observing period EFFECT_1: the source of the satellite data will need to be from a recent observing period ### END
### Instruction: Extract the cause and effect entities from the following sentence: Once a dataset is released to the user community via the Data Portal evolution of the algorithm and further re-processing will be carefully managed ### Assistant: KEYWORD: Once VARIABLE: further re-processing CONDITION: will be carefully managed CAUSE_1: a dataset is released to the user community via the Data Portal AND: and EFFECT_1: evolution of the algorithm EFFECT_2: further re-processing will be carefully managed ### END
### Instruction: Extract the cause and effect entities from the following sentence: If that funding were not available or insufficient in future years, base funds at the locations and/or discretionary funds from the Area and Headquarters funds would support the activity but the timelines would be adjusted ### Assistant: KEYWORD: If VARIABLE: the timelines CONDITION: would be adjusted OR: or CAUSE_1: that funding were not available CAUSE_2: insufficient in future years EFFECT_3: the timelines would be adjusted AND: but EFFECT_1: base funds at the locations EFFECT_2: discretionary funds from the Area and Headquarters funds would support the activity NEGATION: were not ### END
### Instruction: Extract the cause and effect entities from the following sentence: STEWARDS will provide web-based geographic information tools to allow site-specific data to be viewed within their spatial context ### Assistant: KEYWORD: to allow VARIABLE: site-specific data CONDITION: to be viewed within their spatial context. CAUSE_1: STEWARDS will provide web-based geographic information tools EFFECT_1: site-specific data to be viewed within their spatial context ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where there is an exception, the software/hardware requirement should be specified for a particular application ### Assistant: KEYWORD: Where CONDITION: should be specified for a particular application CAUSE_1: there is an exception VARIABLE: there EFFECT_1: the software/hardware requirement should be specified for a particular application. ### END
### Instruction: Extract the cause and effect entities from the following sentence: Further requirements gathering is needed to get real-world estimates of such data storage needs ### Assistant: CONDITION: to get KEYWORD: is needed VARIABLE: Further requirements CAUSE_1: Further requirements gathering EFFECT_1: to get real-world estimates of such data storage needs ### END
### Instruction: Extract the cause and effect entities from the following sentence: HR-2.1: Load Balancing - Application Servers and Database servers must be load balanced at the application level to ensure maximum stability and availability ### Assistant: NOT_RELEVANT: HR-2.1: VARIABLE: availability AND: and CONDITION: to ensure CAUSE_1: Load Balancing - Application Servers CAUSE_2: Database servers must be load balanced at the application level EFFECT_1: to ensure maximum stability EFFECT_2: availability ### END
### Instruction: Extract the cause and effect entities from the following sentence: Data will be subject to quality assurance prior to either initial or recurring uploading to STEWARDS ### Assistant: CONDITION: will be subject to quality assurance OR: either VARIABLE: Data EFFECT_1: Data will be subject to quality assurance CAUSE_1: initial CAUSE_2: recurring uploading to STEWARDS NEGATION: prior to ### END
### Instruction: Extract the cause and effect entities from the following sentence: Quality assurance checking at the local sites may be performed before or during the standard exchange file conversion phase ### Assistant: KEYWORD: during OR: or VARIABLE: Quality assurance checking at the local sites CAUSE_1: the standard exchange file conversion phase CONDITION: may be performed EFFECT_1: Quality assurance checking at the local sites may be performed ### END
### Instruction: Extract the cause and effect entities from the following sentence: A general quality assurance checking, not as specific as at the local sites, will be carried out during the data uploading stage ### Assistant: KEYWORD: during VARIABLE: A general quality assurance checking CAUSE_1: the data uploading stage NOT_RELEVANT: not as specific as at the local sites, CONDITION: will be carried out EFFECT_1: will be carried out ### END
### Instruction: Extract the cause and effect entities from the following sentence: In this manner, all the data files from the local sites will have consistent formats when they are stored in the central site ### Assistant: KEYWORD: when CONDITION: will have consistent formats CAUSE_1: they are stored in the central site VARIABLE: they EFFECT_1: all the data files from the local sites will have consistent formats NOT_RELEVANT: In this manner ### END
### Instruction: Extract the cause and effect entities from the following sentence: The central database will allow users to access the data of individual watersheds, sub-watersheds on a station-by-station basis, or a collection of stations if the entire dataset is desired ### Assistant: KEYWORD: if VARIABLE: The central database CONDITION: a collection of stations CAUSE_1: the entire dataset is desired OR: or EFFECT_1: The central database will allow users to access the data of individual watersheds EFFECT_2: sub-watersheds on a station-by-station basis EFFECT_3: a collection of stations ### END
### Instruction: Extract the cause and effect entities from the following sentence: The system will have a simple interface, consistent with industry standard interfaces, to eliminate the need for user training of infrequent users ### Assistant: CONDITION: will have a simple interface, consistent with industry standard interfaces VARIABLE: The system EFFECT_1: to eliminate the need for user training of infrequent users CAUSE_1: The system will have a simple interface, consistent with industry standard interfaces ### END
### Instruction: Extract the cause and effect entities from the following sentence: This system will include a warning message when a low transmission speed is detected, and a non-graphical interface option will be available ### Assistant: KEYWORD: when VARIABLE: a non-graphical interface option CONDITION: will be available CAUSE_1: a low transmission speed is detected AND: and EFFECT_1: This system will include a warning message EFFECT_2: a non-graphical interface option will be available ### END
### Instruction: Extract the cause and effect entities from the following sentence: Functional requirements are also called behavioral requirements because they address what the system does ### Assistant: KEYWORD: because CONDITION: are also called behavioral requirements CAUSE_1: they address what the system does VARIABLE: they EFFECT_1: Functional requirements are also called behavioral requirements ### END
### Instruction: Extract the cause and effect entities from the following sentence: Completion/Confirmation messages should be displayed when the application processes the data successfully ### Assistant: KEYWORD: when VARIABLE: Completion/Confirmation messages CONDITION: should be displayed CAUSE_1: the application processes the data successfully EFFECT_1: Completion/Confirmation messages should be displayed ### END
### Instruction: Extract the cause and effect entities from the following sentence: During the logical system design phase, a logical data system (all system components and their relationships) will be constructed and validated ### Assistant: KEYWORD: During VARIABLE: a logical data system CAUSE_1: the logical system design phase CONDITION: validated AND: and NOT_RELEVANT: (all system components and their relationships) EFFECT_1: will be constructed EFFECT_2: validated ### END
### Instruction: Extract the cause and effect entities from the following sentence: The SRD is a living document, and it is intended that the document undergoes the necessary pressures of review and subsequent revision, during the lifetime of Phase 1 ### Assistant: KEYWORD: during CONDITION: subsequent revision CAUSE_1: the lifetime of Phase 1 VARIABLE: the document EFFECT_1: The SRD is a living document AND: and NOT_RELEVANT: it is intended that EFFECT_2: the document undergoes the necessary pressures of review EFFECT_3: subsequent revision ### END
### Instruction: Extract the cause and effect entities from the following sentence: Where change takes place in the SRD, those deliverables which depend on the SRD must be appropriately updated ### Assistant: KEYWORD: which CONDITION: depend on the SRD VARIABLE: deliverables CAUSE_1: change takes place in the SRD EFFECT_1: must be appropriately updated CAUSE_2: depend on the SRD ### END
### Instruction: Extract the cause and effect entities from the following sentence: A set of system requirements is elicited and refined based on a judicious mapping with DUACS, prior to other inputs being considered ### Assistant: NEGATION: prior to VARIABLE: A set of system requirements CONDITION: refined based on a judicious mapping with DUACS CAUSE_1: other inputs being considered AND: and EFFECT_1: A set of system requirements is elicited EFFECT_2: refined based on a judicious mapping with DUACS ### END
### Instruction: Extract the cause and effect entities from the following sentence: Metadata will be associated with SLCCI in order to describe all output sea level products ### Assistant: KEYWORD: in order to CONDITION: will be associated with SLCCI VARIABLE: Metadata CAUSE_1: describe all output sea level products EFFECT_1: Metadata will be associated with SLCCI ### END
### Instruction: Extract the cause and effect entities from the following sentence: While interacting with the system, he is to be considered as an actor and uses specific system capabilities ### Assistant: KEYWORD: While CONDITION: uses specific system capabilities VARIABLE: he CAUSE_1: interacting with the system AND: and EFFECT_1: he is to be considered as an actor EFFECT_2: uses specific system capabilities ### END
### Instruction: Extract the cause and effect entities from the following sentence: The SLCCI users relate to climatology research and so do not require degraded-quality NRT data products ### Assistant: KEYWORD: and so NEGATION: do not CONDITION: relate to climatology research VARIABLE: The SLCCI users CAUSE_1: The SLCCI users relate to climatology research EFFECT_1: do not require degraded-quality NRT data products ### END
### Instruction: Extract the cause and effect entities from the following sentence: If the outcome of such an analysis is positive, it is likely that USCRN data can be classified in one of the higher classes in future ### Assistant: KEYWORD: If VARIABLE: USCRN data CONDITION: can be classified in one of the higher classes in future CAUSE_1: the outcome of such an analysis is positive NOT_RELEVANT: it is likely that EFFECT_1: USCRN data can be classified in one of the higher classes in future ### END
### Instruction: Extract the cause and effect entities from the following sentence: Sensors can sample data periodically (eg: sound recording) or trigger events when a given condition is met ### Assistant: KEYWORD: when VARIABLE: a given condition CONDITION: trigger events CAUSE_1: a given condition is met NOT_RELEVANT: Sensors can sample data periodically (eg: sound recording) OR: or EFFECT_1: trigger events ### END
### Instruction: Extract the cause and effect entities from the following sentence: These can be used to play a given sound when a certain message is received ### Assistant: KEYWORD: when VARIABLE: These CONDITION: can be used to play a given sound CAUSE_1: a certain message is received EFFECT_1: These can be used to play a given sound ### END
### Instruction: Extract the cause and effect entities from the following sentence: It uses information in the link and adaptation layers to compress network- and transport-layer headers ### Assistant: VARIABLE: network CONDITION: adaptation layers AND: and CAUSE_1: It uses information in the link CAUSE_2: adaptation layers EFFECT_1: to compress network- EFFECT_2: transport-layer headers ### END
### Instruction: Extract the cause and effect entities from the following sentence: Because every INSTEON devices are flat, they do not require network supervision (network controllers and routing tables are not required) ### Assistant: KEYWORD: Because VARIABLE: they CONDITION: require network supervision CAUSE_1: every INSTEON devices are flat EFFECT_1: they do not require network supervision NOT_RELEVANT: (network controllers and routing tables are not required) NEGATION: do not ### END
### Instruction: Extract the cause and effect entities from the following sentence: With a growing selection of wireless networking alternatives, users are no longer confined to wired installations, and with cost-effective and reliable wireless products emerging based on global standards, users are no longer restricted to proprietary wireless approaches ### Assistant: KEYWORD: with CONDITION: longer restricted to proprietary wireless approaches VARIABLE: a CAUSE_1: a growing selection of wireless networking alternatives EFFECT_1: users are no longer confined to wired installations AND: and CAUSE_2: cost-effective CAUSE_3: reliable wireless products emerging based on global standards EFFECT_2: users are no longer restricted to proprietary wireless approaches NEGATION: are no ### END
### Instruction: Extract the cause and effect entities from the following sentence: Applications that are based on onthologies could store links to their OWL descriptors ### Assistant: VARIABLE: Applications CONDITION: could store links to their OWL descriptors CAUSE_1: are based on onthologies EFFECT_1: could store links to their OWL descriptors. KEYWORD: that ### END
### Instruction: Extract the cause and effect entities from the following sentence: Dynamic DNS: It is possible to dynamically send updates to the DNS server(s) whenever the situation of the internal network changes ### Assistant: KEYWORD: whenever VARIABLE: It CONDITION: is possible to dynamically send updates to the DNS server(s) CAUSE_1: the situation of the internal network changes NOT_RELEVANT: Dynamic DNS: EFFECT_1: It is possible to dynamically send updates to the DNS server(s) ### END
### Instruction: Extract the cause and effect entities from the following sentence: Devices that do not have direct access to an IP network will need to be accessible through a gateway ### Assistant: VARIABLE: Devices NEGATION: do not CONDITION: will need to be accessible through a gateway EFFECT_1: will need to be accessible through a gateway CAUSE_1: Devices KEYWORD: that ### END
### Instruction: Extract the cause and effect entities from the following sentence: Instead, protocols that are based on multicast discovery schemes like DPWS need to provide a way to propagate multicast messages outside the internal network ### Assistant: NOT_RELEVANT: like DPWS VARIABLE: protocols CONDITION: need to provide a way to propagate multicast messages outside the internal network. CAUSE_1: are based on multicast discovery schemes EFFECT_1: need to provide a way to propagate multicast messages outside the internal network KEYWORD: that ### END
### Instruction: Extract the cause and effect entities from the following sentence: The Devices description working group has developed a core vocabulary to adapt content in Mobile Web ### Assistant: VARIABLE: content in Mobile Web CONDITION: to adapt CAUSE_1: The Devices description working group has developed a core vocabulary EFFECT_1: to adapt content in Mobile Web ### END
### Instruction: Extract the cause and effect entities from the following sentence: When an IP network is set up, it is possible to retrieve data from and manipulate devices through their IP interfaces ### Assistant: KEYWORD: it is possible VARIABLE: devices CONDITION: through their IP interfaces CAUSE_1: an IP network is set up AND: and EFFECT_1: to retrieve data from EFFECT_2: manipulate devices through their IP interfaces ### END
### Instruction: Extract the cause and effect entities from the following sentence: In general, pollution of the application space should be avoided especially when devices from different networking technologies are expected to communicate together (they will usually do that, either directly or indirectly through an IP network) ### Assistant: KEYWORD: especially when VARIABLE: pollution of the application space CONDITION: should be avoided CAUSE_1: devices from different networking technologies are expected to communicate together NOT_RELEVANT: In general EFFECT_1: pollution of the application space should be avoided ### END
### Instruction: Extract the cause and effect entities from the following sentence: SIP: the SIP protocol is ideal whenever data must be acquired in a conversational manner ### Assistant: KEYWORD: whenever VARIABLE: data CONDITION: must be acquired in a conversational manner EFFECT_1: the SIP protocol is ideal CAUSE_1: data must be acquired in a conversational manner NOT_RELEVANT: SIP: ### END
### Instruction: Extract the cause and effect entities from the following sentence: The SIP URL of a given stream could, if not previously known, be registered under a DNS name using a DNS TXT record ### Assistant: KEYWORD: if NEGATION: not CONDITION: be registered under a DNS name using a DNS TXT record CAUSE_1: not previously known VARIABLE: The SIP URL of a given stream EFFECT_1: be registered under a DNS name using a DNS TXT record ### END
### Instruction: Extract the cause and effect entities from the following sentence: In case SOAP is used a device will expose its interface through a description language called WSDL ### Assistant: KEYWORD: In case VARIABLE: a device CONDITION: will expose its interface through a description language called WSDL CAUSE_1: SOAP is used EFFECT_1: a device will expose its interface through a description language called WSDL ### END
### Instruction: Extract the cause and effect entities from the following sentence: This description language relies on XSD (XML Schema Definition) in order to describe the structure of the data that can be exchanged with a device ### Assistant: KEYWORD: in order VARIABLE: the structure of the data that can be exchanged with a device CONDITION: to describe CAUSE_1: This description language relies on XSD NOT_RELEVANT: (XML Schema Definition) EFFECT_1: to describe the structure of the data that can be exchanged with a device ### END
### Instruction: Extract the cause and effect entities from the following sentence: However, given the faulty nature of hardware, SOAP clients should not assume the services to be always up and running, and provide a buffer solution for downtimes ### Assistant: KEYWORD: given NOT_RELEVANT: However CONDITION: provide a buffer solution for downtimes VARIABLE: the CAUSE_1: the faulty nature of hardware NEGATION: not AND: and EFFECT_1: SOAP clients should not assume the services to be always up and running EFFECT_2: provide a buffer solution for downtimes ### END
### Instruction: Extract the cause and effect entities from the following sentence: REST: With REST, it is possible for every device to maintain its own resources inside the network, and to store its exported data over there ### Assistant: KEYWORD: for VARIABLE: its own resources NOT_RELEVANT: REST CONDITION: to maintain CAUSE_1: REST EFFECT_1: to maintain its own resources inside the network AND: and EFFECT_2: to store its exported data over there CAUSE_2: every device ### END
### Instruction: Extract the cause and effect entities from the following sentence: This has the advantage that data is available even when a device is offline ### Assistant: KEYWORD: even when VARIABLE: data CONDITION: is available CAUSE_1: a device is offline EFFECT_1: data is available NOT_RELEVANT: This has the advantage that ### END
### Instruction: Extract the cause and effect entities from the following sentence: Likewise, control information could be put inside a resource by a device-controlling application, and have the device periodically read them (or being notified through HTTP Server Push), especially after it goes online ### Assistant: VARIABLE: control information CONDITION: have the device periodically read them CAUSE_1: it goes online NOT_RELEVANT: (or being notified through HTTP Server Push) EFFECT_1: control information could be put inside a resource by a device-controlling application AND: and EFFECT_2: have the device periodically read them KEYWORD: especially NEGATION: after ### END
### Instruction: Extract the cause and effect entities from the following sentence: However, the problem could be mitigated if wired networking is used and the wired network is situated at a secure location ### Assistant: KEYWORD: if VARIABLE: the problem CONDITION: could be mitigated AND: and EFFECT_1: the problem could be mitigated NOT_RELEVANT: However CAUSE_1: wired networking is used CAUSE_2: the wired network is situated at a secure location ### END
### Instruction: Extract the cause and effect entities from the following sentence: Devices could communicate between entities using different networking protocols only if a protocol translation occurs ### Assistant: KEYWORD: only if VARIABLE: Devices CONDITION: could communicate between entities using different networking protocols CAUSE_1: a protocol translation occurs EFFECT_1: Devices could communicate between entities using different networking protocols ### END
### Instruction: Extract the cause and effect entities from the following sentence: Modified TCP allows running TCP/IP protocol suite directly in the WSNs [54] ### Assistant: VARIABLE: TCP/IP protocol suite CONDITION: directly in the WSNs CAUSE_1: Modified TCP EFFECT_1: running TCP/IP protocol suite directly in the WSNs NOT_RELEVANT: [54] KEYWORD: allows ### END
### Instruction: Extract the cause and effect entities from the following sentence: On the other hand, by using the combined information, the virtual sensors have also the capability of reasoning by using fusion techniques5 ### Assistant: NOT_RELEVANT: On the other hand CONDITION: have also the capability of reasoning by using fusion techniques5 VARIABLE: the virtual sensors CAUSE_1: using the combined information EFFECT_1: the virtual sensors have also the capability of reasoning by using fusion techniques5 KEYWORD: by ### END
### Instruction: Extract the cause and effect entities from the following sentence: This first technique uses the time of transmission, the known signal propagation speed and the time of arrival of such signal in order to compute the distance between two nodes in the WSN ### Assistant: KEYWORD: in order CONDITION: the known signal propagation speed VARIABLE: This first technique CAUSE_1: to compute the distance between two nodes in the WSN AND: and EFFECT_1: This first technique uses the time of transmission EFFECT_2: the known signal propagation speed EFFECT_3: the time of arrival of such signal ### END
### Instruction: Extract the cause and effect entities from the following sentence: However the main drawback is that it is difficult to precisely record the arrival time of radio signals, since they travel close to the light speed ### Assistant: KEYWORD: since VARIABLE: the arrival time of radio signals CONDITION: is difficult to precisely record CAUSE_1: they travel close to the light speed NOT_RELEVANT: However the main drawback is that it EFFECT_1: is difficult to precisely record the arrival time of radio signals ### END
### Instruction: Extract the cause and effect entities from the following sentence: It does not scale well in large networks and problems may arise due to environmental conditions ### Assistant: KEYWORD: due to VARIABLE: problems CAUSE_1: environmental conditions CONDITION: may arise NEGATION: not EFFECT_1: It does not scale well in large networks AND: and EFFECT_2: problems may arise ### END