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values | extractor_typology stringclasses 1
value | specificity stringclasses 3
values | typology stringclasses 11
values | usage stringlengths 6 756 ⌀ | valid_data_mention bool 2
classes | judge_error stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
reliefweb:001624 | 13 | 0 | national census | 1,444 | 1,459 | descriptive | descriptive | other | the Government announced its intention to proceed with a ⟦national census⟧ in preparation for the national elections. | false | null | |
reliefweb:001624 | 28 | 0 | Refugees in and from DRC | 1,316 | 1,340 | descriptive | named | other | ⟦Refugees in and from DRC⟧, 1990-1999 | false | null | |
reliefweb:001624 | 2 | 0 | United Nations Population Division | 345 | 379 | named | named | estimates | The ⟦United Nations Population Division⟧ estimates the population to be 37,405,000 in 1990 | true | null | |
reliefweb:001624 | 36 | 1 | 1999 Country Reports on Human Rights Practices | 613 | 659 | named | named | other | **U.S. Department of State** : ⟦1999 Country Reports on Human Rights Practices⟧, DRC, 25 February 2000 | false | null | |
reliefweb:001625 | 10 | 0 | Registered Incidents | 5 | 25 | named | descriptive | other | #### ⟦Registered Incidents⟧: October - December 2016 | false | null | |
reliefweb:001625 | 11 | 0 | Gender-Based Violence Information Management System | 797 | 848 | named | named | system | the ⟦Gender-Based Violence Information Management System⟧ (GBV-IMS), used by humanitarian service providers and managed by UNFPA, recorded 575 incidents of GBV | true | null | |
reliefweb:001625 | 11 | 1 | GBV – IMS data | 1,475 | 1,489 | named | named | system | the ⟦GBV – IMS data⟧ cannot capture reports of service and GBV incidents | true | null | |
reliefweb:001625 | 11 | 2 | data for December 2016 | 713 | 735 | vague | vague | other | The ⟦data for December 2016⟧ is not complete yet | true | null | |
reliefweb:001625 | 12 | 0 | Coordinated GBV and Protection Safety Audit | 685 | 728 | named | named | other | See “⟦Coordinated GBV and Protection Safety Audit⟧”, 3 to 11 November 2016 | false | null | |
reliefweb:001625 | 12 | 0 | GBVIMS | 816 | 822 | named | named | system | The ⟦GBVIMS⟧ was rolled out in South Sudan in 2014 | true | null | |
reliefweb:001625 | 12 | 0 | GBVIMS data | 909 | 920 | named | named | database | ⟦GBVIMS data⟧ that is shared is only from reported cases | true | null | |
reliefweb:001625 | 12 | 0 | GBVIMS | 1,216 | 1,222 | named | named | system | service providers who use the ⟦GBVIMS⟧ for data collection | true | null | |
reliefweb:001625 | 13 | 0 | Mine Action database | 894 | 914 | named | named | database | the known hazards recorded in the ⟦Mine Action database⟧ | true | null | |
reliefweb:001625 | 13 | 1 | Known Hazardous Areas and Number of Known Hazards | 910 | 959 | descriptive | descriptive | other | ⟦Known Hazardous Areas and Number of Known Hazards⟧ as of December 2016 | false | null | |
reliefweb:001625 | 13 | 1 | Known Hazardous Areas and Number of Known Hazards | 1,015 | 1,064 | descriptive | descriptive | other | ⟦Known Hazardous Areas and Number of Known Hazards⟧ as of December 2016 | false | null | |
reliefweb:001625 | 1 | 2 | Small Arms Survey report | 1,274 | 1,298 | named | named | other | See ⟦Small Arms Survey report⟧, December 2016 | false | null | |
reliefweb:001625 | 2 | 2 | quantitative data | 1,533 | 1,550 | vague | vague | other | UNHCR teams are collecting ⟦quantitative data⟧ | true | null | |
reliefweb:001625 | 2 | 4 | DRC Intention Surveys | 417 | 438 | named | named | survey | See ⟦DRC Intention Surveys⟧ for Melut POC site and Palioch school | true | null | |
reliefweb:001625 | 2 | 4 | MSF survey in Malakal POC | 496 | 521 | named | named | survey | and ⟦MSF survey in Malakal POC⟧ (May 2016) | true | null | |
reliefweb:001625 | 2 | 4 | UNHCR Block Survey Report | 774 | 799 | named | named | survey | See ⟦UNHCR Block Survey Report⟧ (POC 1&3), 24 January 2017 | true | null | |
reliefweb:001625 | 3 | 0 | monitoring of new arrivals in Uganda | 1,412 | 1,448 | descriptive | descriptive | other | From ⟦monitoring of new arrivals in Uganda⟧, a number have been living as IDPs | false | null | |
reliefweb:001625 | 3 | 1 | monitoring of new arrivals in Uganda | 105 | 141 | descriptive | descriptive | other | From ⟦monitoring of new arrivals in Uganda⟧, a number have been living as IDPs | false | null | |
reliefweb:001625 | 4 | 0 | community assessments | 815 | 836 | descriptive | descriptive | survey | by ⟦community assessments⟧, it is estimated there are well over 100,000 IDPs in Juba | true | null | |
reliefweb:001625 | 4 | 1 | PC Trends report | 1,768 | 1,784 | named | named | report | since the last ⟦PC Trends report⟧ at over 80,000 | true | null | |
reliefweb:001625 | 4 | 2 | PC Trends report | 49 | 65 | named | named | report | From the previous ⟦PC Trends report⟧, less incidents of attacks on civilians | true | null | |
reliefweb:001625 | 4 | 2 | December head count | 354 | 373 | descriptive | descriptive | census | population 29,021 individuals from ⟦December head count⟧ | true | null | |
reliefweb:001625 | 5 | 1 | RRC | 933 | 936 | named | named | other | over 1000 registered with the ⟦RRC⟧ | false | null | |
reliefweb:001625 | 6 | 0 | movement tracking in the POC | 280 | 308 | descriptive | descriptive | other | ⟦movement tracking in the POC⟧ identified persons returning to the site | true | null | |
reliefweb:001625 | 6 | 0 | intention surveys | 397 | 414 | descriptive | descriptive | survey | conducting further ⟦intention surveys⟧ to establish triggers for movement | true | null | |
reliefweb:001625 | 6 | 1 | vulnerability assessment conducted by the Protection Cluster | 1,632 | 1,692 | descriptive | descriptive | survey | A ⟦vulnerability assessment conducted by the Protection Cluster⟧ in December revealed that some 507 households | true | null | |
reliefweb:001625 | 6 | 2 | IOM headcount | 1,481 | 1,494 | named | named | census | according to a recent ⟦IOM headcount⟧ | true | null | |
reliefweb:001625 | 6 | 3 | UNMISS –GHANBATT figures | 704 | 728 | named | named | other | according to ⟦UNMISS –GHANBATT figures⟧ | true | null | |
reliefweb:001625 | 8 | 1 | Family Tracing and Reunification database | 1,360 | 1,401 | named | named | database | Source: ⟦Family Tracing and Reunification database⟧ | true | null | |
reliefweb:001625 | 9 | 0 | Monitoring and Reporting Mechanism | 557 | 591 | named | named | system | reported through the ⟦Monitoring and Reporting Mechanism⟧ (MRM) | true | null | |
reliefweb:001625 | 9 | 1 | Registered Incidents per State | 490 | 520 | descriptive | descriptive | other | ⟦Registered Incidents per State⟧ | false | null | |
reliefweb:001625 | 9 | 1 | Monitoring and Reporting Mechanism | 829 | 863 | named | named | system | Source: ⟦Monitoring and Reporting Mechanism⟧ | true | null | |
reliefweb:001627 | 1 | 1 | Map shows civilian impact hotspots | 395 | 429 | descriptive | descriptive | geospatial | (⟦Map shows civilian impact hotspots⟧ from May 24 to June 6) | true | null | |
reliefweb:001627 | 1 | 2 | civilian impact in Al-Hudaydah governorate | 1,638 | 1,680 | descriptive | descriptive | other | This short assessment seeks to extract analytical highlights from ⟦civilian impact in Al-Hudaydah governorate⟧[1] | false | null | |
reliefweb:001627 | 2 | 0 | CIMP | 1,057 | 1,061 | named | named | other | the displacement recorded by ⟦CIMP⟧[2] from incidents of armed violence directly impacting on civilian homes | true | null | |
reliefweb:001630 | 12 | 1 | Big (Crisis) Data sources | 880 | 905 | descriptive | vague | other | studies mainly used ⟦Big (Crisis) Data sources⟧[3] in manageable case studies | true | null | |
reliefweb:001630 | 13 | 0 | CDO database | 457 | 469 | named | named | database | the World Bank’s economic indicators, NOAA’s ⟦CDO database⟧[4] | true | null | |
reliefweb:001630 | 13 | 0 | conflict database | 513 | 530 | named | descriptive | database | ACLED’s or the university of Uppsala’s ⟦conflict database⟧[5] | true | null | |
reliefweb:001630 | 13 | 0 | QuickBird | 1,075 | 1,084 | named | named | geospatial | Fine-resolution satellite imagery (e.g., WorldView, Pleaides, IKONOS, GEO, and ⟦QuickBird⟧[6]) | true | null | |
reliefweb:001630 | 13 | 0 | training data | 1,515 | 1,528 | vague | descriptive | other | Supervised classification methods usually generate more accurate results, but require ⟦training data⟧[7] from a considerable portion of the area to work correctly | true | null | |
reliefweb:001630 | 13 | 1 | Omdena project | 756 | 770 | named | named | other | These two points can potentially produce bias in the data and limit the scalability of this solution (e.g., see ⟦Omdena project⟧[8]) | false | null | |
reliefweb:001630 | 13 | 1 | remote-sensing nocturnal lights images | 797 | 835 | descriptive | descriptive | geospatial | Levin et al. [22] use ⟦remote-sensing nocturnal lights images⟧[9] to track conflict areas in real-time during the Arab spring | true | null | |
reliefweb:001630 | 14 | 0 | Global Pulse project | 182 | 202 | named | named | other | e.g., a ⟦Global Pulse project⟧[10] conducted in Sudan investigates the correlation between nocturnal lights and poverty rates | false | null | |
reliefweb:001630 | 14 | 0 | EventRegistry | 1,725 | 1,738 | named | named | database | Projects that daily collect large quantities of media in multiple languages are, e.g., ⟦EventRegistry⟧[11] | true | null | |
reliefweb:001630 | 14 | 0 | GDELT project | 1,744 | 1,757 | named | named | database | the ⟦GDELT project⟧[12] | true | null | |
reliefweb:001630 | 14 | 0 | ICEWS | 1,759 | 1,764 | named | named | database | the ⟦ICEWS⟧[13] | true | null | |
reliefweb:001630 | 14 | 0 | Expandable Open Source database | 1,770 | 1,801 | named | named | database | the ⟦Expandable Open Source database⟧[14] (EOS) | true | null | |
reliefweb:001630 | 14 | 0 | Europe Media Monitor | 1,813 | 1,833 | named | named | database | the ⟦Europe Media Monitor⟧[15] (EMM) project | true | null | |
reliefweb:001630 | 14 | 0 | Global Terrorist Database | 1,877 | 1,902 | named | named | database | the ⟦Global Terrorist Database⟧[16] | true | null | |
reliefweb:001630 | 14 | 1 | Expandable Open Source database | 24 | 55 | named | named | database | the ⟦Expandable Open Source database⟧[17] (EOS) | true | null | |
reliefweb:001630 | 14 | 1 | Europe Media Monitor | 67 | 87 | named | named | database | the ⟦Europe Media Monitor⟧[18] (EMM) project | true | null | |
reliefweb:001630 | 14 | 1 | Global Terrorist Database | 131 | 156 | named | named | database | the ⟦Global Terrorist Database⟧[19] | true | null | |
reliefweb:001630 | 14 | 1 | Event data | 601 | 611 | vague | descriptive | other | ⟦Event data⟧[20] from news media outlets have the advantage that they are vetted before publication by the press agency | true | null | |
reliefweb:001630 | 14 | 1 | news article databases | 787 | 809 | descriptive | descriptive | database | ⟦news article databases⟧[21] allow cross-checking facts on an event through multiple reports from multiple press agencies | true | null | |
reliefweb:001630 | 15 | 0 | news databases | 931 | 945 | descriptive | descriptive | database | Another challenge in a broader application of this ⟦news databases⟧[22] are unspecified names of localities | true | null | |
reliefweb:001630 | 15 | 0 | GDELT database | 1,690 | 1,704 | named | named | database | Melachrinos et al. [31] use 240 different event types from the ⟦GDELT database⟧[23] to generate a push-factor-index | true | null | |
reliefweb:001630 | 15 | 0 | Data from news media | 1,775 | 1,795 | descriptive | descriptive | other | ⟦Data from news media⟧[24] are a popular source for early event detection in predictive models of forced displacement | true | null | |
reliefweb:001630 | 15 | 1 | GDELT database | 36 | 50 | named | named | database | from the ⟦GDELT database⟧[1] to generate a push-factor-index | true | null | |
reliefweb:001630 | 15 | 1 | EOS database | 283 | 295 | named | named | database | use event data from the ⟦EOS database⟧[2] to predict movements | true | null | |
reliefweb:001630 | 15 | 1 | event data on violence | 388 | 410 | descriptive | descriptive | other | use ⟦event data on violence⟧[3] from the GDELT database as a proxy | true | null | |
reliefweb:001630 | 15 | 1 | EOS database | 583 | 595 | named | named | database | collect 1.4 million English language news articles from the ⟦EOS database⟧[4] | true | null | |
reliefweb:001630 | 15 | 1 | news article databases | 712 | 734 | descriptive | descriptive | database | use ⟦news article databases⟧[5] to develop early event detection systems as a primary data source | true | null | |
reliefweb:001630 | 15 | 1 | data from social media sites | 1,237 | 1,265 | descriptive | descriptive | other | Although ⟦data from social media sites⟧[6] have one of the lowest signal-to-noise ratios | true | null | |
reliefweb:001630 | 15 | 1 | data from social media sites | 1,472 | 1,500 | descriptive | descriptive | other | analysts can use ⟦data from social media sites⟧[7] in two ways | true | null | |
reliefweb:001630 | 15 | 1 | remote-sensing data | 1,646 | 1,665 | descriptive | descriptive | geospatial | in the likeness with ⟦remote-sensing data⟧[8] of nocturnal lights | true | null | |
reliefweb:001630 | 16 | 0 | user data | 50 | 59 | vague | descriptive | other | Because of the easy and free accessibility of its ⟦user data⟧[9], Twitter has become the most popular social media site | true | null | |
reliefweb:001630 | 16 | 0 | data from the Twitter API | 485 | 510 | descriptive | descriptive | other | Investigation of the ⟦data from the Twitter API⟧[10] show | true | null | |
reliefweb:001630 | 16 | 0 | GDELT database | 758 | 772 | named | named | database | combines data from the ⟦GDELT database⟧[11] with ⟦data from Twitter⟧[12] | true | null | |
reliefweb:001630 | 16 | 0 | data from Twitter | 778 | 795 | descriptive | descriptive | other | with ⟦data from Twitter⟧[12] to develop an algorithm for early event detection | true | null | |
reliefweb:001630 | 16 | 1 | geotagged Flickr photographs | 629 | 657 | descriptive | named | geospatial | use ⟦geotagged Flickr photographs⟧[13] as a proxy for the spread and the intensity of conflicts | true | null | |
reliefweb:001630 | 17 | 0 | news article databases | 112 | 134 | descriptive | descriptive | database | data from ⟦news article databases⟧[14] could additionally be used to try to build prediction models | true | null | |
reliefweb:001630 | 17 | 0 | ACLED | 992 | 997 | named | named | database | Event characteristics can be derived from either traditional data sources, e.g., ⟦ACLED⟧[15] | true | null | |
reliefweb:001630 | 17 | 0 | Big (Crisis) Data | 1,188 | 1,205 | vague | descriptive | other | ⟦Big (Crisis) Data⟧[16] can supplement with valuable and timely information | true | null | |
reliefweb:001630 | 17 | 0 | data from internet searches or social media sites | 1,486 | 1,535 | descriptive | descriptive | other | E.g., ⟦data from internet searches or social media sites⟧[17] that inquire about flight routes or host countries | true | null | |
reliefweb:001630 | 17 | 0 | Internet searches | 1,750 | 1,767 | descriptive | descriptive | other | **⟦Internet searches⟧[18]** ⟦Google Trends⟧[19] tracks its user’s search queries | true | null | |
reliefweb:001630 | 17 | 0 | Google Trends | 1,770 | 1,783 | named | named | system | ⟦Google Trends⟧[19] tracks its user’s search queries | true | null | |
reliefweb:001630 | 17 | 1 | trend data | 246 | 256 | vague | descriptive | other | These ⟦trend data⟧[20] are frequently used in migration studies | true | null | |
reliefweb:001630 | 17 | 1 | Data from Google Trends | 513 | 536 | named | named | system | ⟦Data from Google Trends⟧[21] is free and easily accessible | true | null | |
reliefweb:001630 | 17 | 1 | search data from Google Trends | 817 | 847 | named | named | system | uses ⟦search data from Google Trends⟧[22] to assess South Americans’ migration intentions | true | null | |
reliefweb:001630 | 17 | 1 | demographic data | 1,147 | 1,163 | descriptive | descriptive | other | through ⟦demographic data⟧[23], size of the agricultural sector | true | null | |
reliefweb:001630 | 18 | 0 | Google Trends data | 62 | 80 | named | named | system | uses ⟦Google Trends data⟧[24] to forecast migration | true | null | |
reliefweb:001630 | 18 | 0 | trend data | 1,087 | 1,097 | vague | descriptive | other | isolate the target population in the ⟦trend data⟧[25] to generate trends | true | null | |
reliefweb:001630 | 18 | 0 | Google Correlate | 1,785 | 1,801 | named | named | system | Data from Google Trends and ⟦Google Correlate⟧[26] are an easy to use and easy to up-scale Big (Crisis) Data source | true | null | |
reliefweb:001630 | 18 | 1 | Google Trends data | 164 | 182 | named | named | system | The biggest weakness of ⟦Google Trends data⟧[27] is bias due to limited access to the internet | true | null | |
reliefweb:001630 | 18 | 1 | data on internet penetration rates | 239 | 273 | descriptive | descriptive | indicator | ⟦data on internet penetration rates⟧[28], which could be used to identify and possibly correct biases | true | null | |
reliefweb:001630 | 18 | 1 | Big (Crisis) Data | 651 | 668 | descriptive | descriptive | other | ⟦Big (Crisis) Data⟧[29] offers many potential data sources to capture movements and hidden populations | true | null | |
reliefweb:001630 | 18 | 1 | call data records | 836 | 853 | descriptive | descriptive | administrative | geotagged social media posts, and ⟦call data records⟧[30] are among the most prominent examples | true | null | |
reliefweb:001630 | 19 | 0 | time-series of QuickBird images | 1,388 | 1,419 | named | named | geospatial | used a ⟦time-series of QuickBird images⟧ to map the evolution in population size | true | null | |
reliefweb:001630 | 19 | 1 | remote-sensing data | 249 | 268 | descriptive | descriptive | geospatial | Data gaps due to cloudiness and air pollution are the biggest disturbance in ⟦remote-sensing data⟧ | true | null | |
reliefweb:001630 | 20 | 0 | social media data | 1,662 | 1,679 | vague | descriptive | other | Studies based on ⟦social media data⟧ report mixed results in this respect | true | null | |
reliefweb:001630 | 20 | 0 | Twitter data | 1,724 | 1,736 | named | named | database | Using ⟦Twitter data⟧, Wong et al. [47] try to solve the problem | true | null | |
reliefweb:001630 | 20 | 1 | social media data | 19 | 36 | descriptive | descriptive | other | Studies based on ⟦social media data⟧ report mixed results in this respect | true | null | |
reliefweb:001630 | 20 | 1 | Twitter data | 81 | 93 | named | named | database | Using ⟦Twitter data⟧, Wong et al. [47] try to solve the problem | true | null | |
reliefweb:001630 | 20 | 1 | self-reported location data | 683 | 710 | descriptive | descriptive | other | use geotagged tweets or ⟦self-reported location data⟧ from Twitter users to estimate the number of Venezuelans | true | null | |
reliefweb:001630 | 20 | 1 | data available through the Twitter API | 903 | 941 | descriptive | named | system | the immigration flow from Venezuela can be approximated from the ⟦data available through the Twitter API⟧ | true | null | |
reliefweb:001630 | 20 | 1 | geo-located tweets from Twitter users | 1,461 | 1,498 | descriptive | descriptive | microdata | Collecting ⟦geo-located tweets from Twitter users⟧ during the period July 2012 to June 2015 to derive their travel history | true | null |
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