DDI description: Chinese Covid-19 Aid Campaign 2020 DDI-kuvailu: Kiinan Covid-19 -tukikampanja 2020 4005 Finnish Social Science Data Archive Finnish Social Science Data Archive Finnish Social Science Data Archive 1.0.0 Chinese Covid-19 Aid Campaign 2020 [metadata]. Metadata version 1.0.0 (2025-12-08). Finnish Social Science Data Archive [producer and distributor]. DOI: https://doi.org/10.60686/t-fsd4005; URN: https://urn.fi/urn:nbn:fi:fsd:T-FSD4005 FSD:n aineistokuvailut (FSD metadata records), joiden tekijä on Suomen yhteiskuntatieteellinen tietoarkisto (Finnish Social Science Data Archive), on lisensoitu Creative Commons 1.0 Yleismaailmallinen (CC0 1.0) -lisenssillä. FSD:n aineistokuvailut (FSD metadata records) by Suomen yhteiskuntatieteellinen tietoarkisto (Finnish Social Science Data Archive) are licensed under a Creative Commons 1.0 Universal (CC0 1.0) license. FSD:n aineistokuvailut (FSD metadata records) av Finlands samhällsvetenskapliga dataarkiv (Finnish Social Science Data Archive) är licensierade under en Creative Commons 1.0 Universiell (CC0 1.0) licens. Chinese Covid-19 Aid Campaign 2020 Kiinan Covid-19 -tukikampanja 2020 4005 urn:nbn:fi:fsd:T-FSD4005 10.60686/t-fsd4005 Paltemaa, Lauri 0000-0001-6192-933X Aubié, Hermann 0000-0003-2263-7021 Sookari, Tommi 0000-0003-4916-691X In accordance with the agreement between FSD and the depositor. 323704 Finnish Social Science Data Archive Individual datasets

Individual datasets that do not belong to any series.

 1.0 Paltemaa, Lauri (University of Turku) & Aubié, Hermann (University of Turku) & Sookari, Tommi (University of Turku): Chinese Covid-19 Aid Campaign 2020 [dataset]. Data version 1.0 (2025-11-14). Finnish Social Science Data Archive [distributor]. DOI: https://doi.org/10.60686/t-fsd4005; URN: https://urn.fi/urn:nbn:fi:fsd:T-FSD4005 aid cooperation COVID-19 diplomacy International cooperation international relations associations Social sciences International politics and organisations

The dataset tracks the humanitarian aid campaign that the People's Republic of China launched soon after the eruption of the Covid-19 pandemic in December 2019. The dataset focuses explicitly to Chinese humanitarian aid during the first year of the CCA campaign.

The dataset tracks donations made by Chinese state and non-state actors for the purpose of treating, containing, and alleviating the effects of the Covid-19 pandemic. It includes donations of both protective and medical equipment, along with cash donations and some non-medical items. In total, the dataset covers donations delivered to approximately 180 countries, non-state entities and international/regional organisations, mainly in 2020. General foreign and development aid are not included in the dataset.

The background variables in the dataset are the date of the donation, the recipient country, the region of the recipient country, the name of the recipient, the type of recipient, the name of the donor, and the type of donor.

 Spring 2021 Summer 2022 Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile Colombia Comoros Congo (Brazzaville) Congo (Kinshasa) Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Eswatini Ethiopia Fiji Finland France French Polynesia Gabon Gambia Georgia Germany Ghana Greece Grenada Guinea Guinea-Bissau Guyana Haiti Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Mauritania Mauritius Mexico Micronesia (Federated States of) Moldova Mongolia Montenegro Morocco Mozambique Myanmar (Burma) Namibia Nauru Nepal Netherlands New Caledonia New Zealand Niger Nigeria Niue North Macedonia Norway Oman Pakistan Palau Palestinian Territories Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Samoa São Tomé and Príncipe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea South Sudan Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Vatican City Venezuela Vietnam Yemen Zambia Zimbabwe Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile Colombia Comoros Congo (Brazzaville) Congo (Kinshasa) Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czechia Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Eswatini Ethiopia Fiji Finland France French Polynesia Gabon Gambia Georgia Germany Ghana Greece Grenada Guinea Guinea-Bissau Guyana Haiti Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Mauritania Mauritius Mexico Micronesia (Federated States of) Moldova Mongolia Montenegro Morocco Mozambique Myanmar (Burma) Namibia Nauru Nepal Netherlands New Caledonia New Zealand Niger Nigeria Niue North Macedonia Norway Oman Pakistan Palau Palestinian Territories Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Samoa São Tomé and Príncipe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea South Sudan Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Vatican City Venezuela Vietnam Yemen Zambia Zimbabwe Event/Process/Activity EventOrProcessOrActivity China's global Covid-19 aid campaign Quantitative Cross-section CrossSection Paltemaa, Lauri Aubié, Hermann Sookari, Tommi Other sampling procedure Other

Data collection aimed to achieve as broad coverage as possible of the Chinese Covid-19 aid campaign worldwide. The dataset was manually compiled from open online sources and is based on publicly available information regarding donations made by Chinese state and non-state actors. The donations draw on approximately 2,000 individual sources in more than twenty languages. The sources can roughly be categorized into official websites and social media accounts (Facebook and Twitter) of Chinese embassies; Chinese state media outlets, such as Xinhua and Xinhua Silk Road Information Service, CGTN, Global Times, and People's Daily; Chinese state agencies, including the Ministry of Foreign Affairs and the China International Development Cooperation Agency (CIDCA); as well as other news outlets.

The dataset quantifies the monetary value of the donations. The monetary value is based on a) a value indicated by the source, b) a value calculated on the basis of price estimate and the number of donated items. If the number of donated items was indicated in the source, this was used to estimate the value. Otherwise, the number of donated items was estimated for example by using photographs of donation ceremonies and the donated items.

Donations with multiple donors and/or recipients were disaggregated at least at the level of donor/recipient types, or at the level of individual donors/recipients if possible. If the number of donated items by donor/recipient (groups) was known, that was recorded to the dataset, otherwise the number of donated items was divided by donors/recipients.

All monetary values are in United States dollars. If the source indicated the value in a different currency, it was converted to USD at the exchange rate applicable at the time of the donation. Additional information can be found in the attachments.

 Content coding ContentCoding Data collection guidelines: Secondary data collection guide DataCollectionGuidelines.SecondaryDataCollectionGuide There are no weight variables in the data. General foreign and development aid are not included in the dataset, nor are donations to the Chinese diaspora. Besides donations, China sold the same items and equipment across the globe. The commercial exports of these medical and personal protective equipment are not included in the dataset. In case a donation included both medical and non-medical items, their values were calculated separately in accordance with the two variables estimating donation value. If a donation included both medical and non-medical items and the total value was given, but the donation was not itemized, the values of medical and non-medical items were assumed to be even. Detailed and specific data description in Finnish and English. The data are checked and processed in detail. If necessary, the data are anonymised. Finnish Social Science Data Archive The dataset is (A) openly available for all users without registration (CC BY 4.0). The data and its creators shall be cited in all publications and presentations for which the data have been used. The bibliographic citation may be in the form suggested by the archive or in the form required by the publication. Notify FSD of all publications where you have used the data by sending the citation information to user-services.fsd@tuni.fi. The original data creators and the archive bear no responsibility for any results or interpretations arising from the reuse of the data. Aubié, Hermann, Lauri Paltemaa, and Tommi Sookari. 2025. 'China's Covid-19 Aid Diplomacy in 2020: Patterns and Motivations'. The Hague Journal of Diplomacy 20 (1): 69-100. https://doi.org/10.1163/1871191x-bja10200. China's Covid-19 Aid Diplomacy in 2020: Patterns and Motivations 10.1163/1871191x-bja10200 Paltemaa, Lauri, Hermann Aubié, and Tommi Sookari. 2025. 'Building the Image of China as a 'Responsible Major Country” in Advanced Economies: Did Beijing's Covid-19 Aid Campaign Work?' European Journal of East Asian Studies 24 (1): 26-50. https://doi.org/10.1163/15700615-02401006. Building the Image of China as a 'Responsible Major Country” in Advanced Economies: Did Beijing's Covid-19 Aid Campaign Work? 10.1163/15700615-02401006 daF4005_eng.sav 2623 39 SPSS Data File [fsd_no] FSD study number FSD study number 2623 4005 4005 4005.00 .000 [fsd_vr] FSD edition number FSD edition number 2623 1.0 1.0 1.000 .0000 [fsd_id] FSD case id FSD case id 2623 1 2623 1312.00 757.339 [fsd_li] FSD license CC BY 4.0 FSD license CC BY 4.0 [country] Recipient country Recipient country 2623 1 181 91.11 51.294 1 Afghanistan 8 2 Albania 6 3 Algeria 10 4 Angola 15 5 Antigua & Barbuda 2 6 Argentina 25 7 Armenia 1 8 Australia 5 9 Austria 9 10 Azerbaijan 9 11 Bahamas 5 12 Bangladesh 9 13 Barbados 8 14 Belarus 13 15 Belgium 10 16 Belize 4 17 Benin 17 18 Bhutan 1 19 Bolivia 10 20 Bosnia & Herzegovina 19 21 Botswana 14 22 Brazil 78 23 Brunei 11 24 Bulgaria 11 25 Burkina Faso 8 26 Burundi 8 27 Cambodia 38 28 Cameroon 8 29 Canada 12 30 Cape Verde 7 31 Central African Republic 29 32 Chad 16 33 Chile 16 34 Colombia 34 35 Comoros 3 36 Congo - Brazzaville 13 37 Congo - Kinshasa 13 38 Costa Rica 26 39 Côte d'Ivoire 16 40 Croatia 19 41 Cuba 16 42 Cyprus 14 43 Czechia 24 44 Denmark 2 45 Djibouti 9 46 Dominica 2 47 Dominican Republic 25 48 Ecuador 56 49 Egypt 14 50 El Salvador 27 51 Equatorial Guinea 13 52 Estonia 1 53 Eswatini 2 54 Ethiopia 20 55 Fiji 18 56 Finland 1 57 France 51 58 French Polynesia 1 59 Gabon 7 60 Gambia 10 61 Georgia 6 62 Germany 45 63 Ghana 24 64 Greece 13 65 Grenada 15 66 Guinea 7 67 Guinea-Bissau 11 68 Guyana 15 69 Haiti 3 70 Hungary 17 71 Iceland 4 72 India 10 73 Indonesia 25 74 Iran 25 75 Iraq 7 76 Ireland 16 77 Israel 4 78 Italy 55 79 Jamaica 19 80 Japan 48 81 Jordan 4 82 Kazakhstan 5 83 Kenya 11 84 Kiribati 5 85 Kuwait 2 86 Kyrgyzstan 17 87 Laos 16 88 Latvia 6 89 Lebanon 7 90 Lesotho 6 91 Liberia 26 92 Libya 4 93 Lithuania 4 94 Luxembourg 16 95 Madagascar 12 96 Malawi 21 97 Malaysia 34 98 Maldives 6 99 Mali 8 100 Malta 7 101 Mauritania 19 102 Mauritius 5 103 Mexico 21 104 Micronesia (Federated States of) 7 105 Moldova 11 106 Mongolia 15 107 Montenegro 15 108 Morocco 10 109 Mozambique 9 110 Myanmar (Burma) 67 111 Namibia 23 112 Nauru 1 113 Nepal 9 114 Netherlands 4 115 New Caledonia 1 116 New Zealand 1 117 Niger 9 118 Nigeria 8 119 Niue 1 120 North Macedonia 14 121 Norway 3 122 Oman 1 123 Pakistan 30 124 Palau 1 125 Palestinian Territories 7 126 Panama 71 127 Papua New Guinea 15 128 Paraguay 1 129 Peru 44 130 Philippines 36 131 Poland 11 132 Portugal 18 133 Qatar 1 134 Romania 15 135 Russia 9 136 Rwanda 11 137 Samoa 7 138 São Tomé & Príncipe 11 139 Saudi Arabia 3 140 Senegal 13 141 Serbia 22 142 Seychelles 10 143 Sierra Leone 12 144 Singapore 13 145 Slovakia 13 146 Slovenia 12 147 Solomon Islands 15 148 Somalia 2 149 South Africa 17 150 South Korea 44 151 South Sudan 13 152 Spain 52 153 Sri Lanka 13 154 Sudan 6 155 Suriname 14 156 Sweden 1 157 Switzerland 4 158 Syria 6 159 Tajikistan 6 160 Tanzania 8 161 Thailand 20 162 Timor-Leste 28 163 Togo 13 164 Tonga 2 165 Trinidad & Tobago 17 166 Tunisia 15 167 Turkey 4 168 Uganda 12 169 Ukraine 6 170 United Arab Emirates 3 171 United Kingdom 26 172 United States 45 173 Uruguay 36 174 Uzbekistan 8 175 Vanuatu 11 176 Vatican City 1 177 Venezuela 10 178 Vietnam 12 179 Yemen 7 180 Zambia 19 181 Zimbabwe 22 [iso3] Recipient country ISO 3166-1 alpha-3 code Recipient country ISO 3166-1 alpha-3 code 2623 1 181 89.29 49.996 1 AFG 8 2 AGO 15 3 ALB 6 4 ARE 3 5 ARG 25 6 ARM 1 7 ATG 2 8 AUS 5 9 AUT 9 10 AZE 9 11 BDI 8 12 BEL 10 13 BEN 17 14 BFA 8 15 BGD 9 16 BGR 11 17 BHS 5 18 BIH 19 19 BLR 13 20 BLZ 4 21 BOL 10 22 BRA 78 23 BRB 8 24 BRN 11 25 BTN 1 26 BWA 14 27 CAF 29 28 CAN 12 29 CHE 4 30 CHL 16 31 CIV 16 32 CMR 8 33 COD 13 34 COG 13 35 COL 34 36 COM 3 37 CPV 7 38 CRI 26 39 CUB 16 40 CYP 14 41 CZE 24 42 DEU 45 43 DJI 9 44 DMA 2 45 DNK 2 46 DOM 25 47 DZA 10 48 ECU 56 49 EGY 14 50 ESP 52 51 EST 1 52 ETH 20 53 FIN 1 54 FJI 18 55 FRA 51 56 FSM 7 57 GAB 7 58 GBR 26 59 GEO 6 60 GHA 24 61 GIN 7 62 GMB 10 63 GNB 11 64 GNQ 13 65 GRC 13 66 GRD 15 67 GUY 15 68 HRV 19 69 HTI 3 70 HUN 17 71 IDN 25 72 IND 10 73 IRL 16 74 IRN 25 75 IRQ 7 76 ISL 4 77 ISR 4 78 ITA 55 79 JAM 19 80 JOR 4 81 JPN 48 82 KAZ 5 83 KEN 11 84 KGZ 17 85 KHM 38 86 KIR 5 87 KOR 44 88 KWT 2 89 LAO 16 90 LBN 7 91 LBR 26 92 LBY 4 93 LKA 13 94 LSO 6 95 LTU 4 96 LUX 16 97 LVA 6 98 MAR 10 99 MDA 11 100 MDG 12 101 MDV 6 102 MEX 21 103 MKD 14 104 MLI 8 105 MLT 7 106 MMR 67 107 MNE 15 108 MNG 15 109 MOZ 9 110 MRT 19 111 MUS 5 112 MWI 21 113 MYS 34 114 NAM 23 115 NCL 1 116 NER 9 117 NGA 8 118 NIU 1 119 NLD 4 120 NOR 3 121 NPL 9 122 NRU 1 123 NZL 1 124 OMN 1 125 PAK 30 126 PAN 71 127 PER 44 128 PHL 36 129 PLW 1 130 PNG 15 131 POL 11 132 PRT 18 133 PRY 1 134 PSE 7 135 PYF 1 136 QAT 1 137 ROU 15 138 RUS 9 139 RWA 11 140 SAU 3 141 SDN 6 142 SEN 13 143 SGP 13 144 SLB 15 145 SLE 12 146 SLV 27 147 SOM 2 148 SRB 22 149 SSD 13 150 STP 11 151 SUR 14 152 SVK 13 153 SVN 12 154 SWE 1 155 SWZ 2 156 SYC 10 157 SYR 6 158 TCD 16 159 TGO 13 160 THA 20 161 TJK 6 162 TLS 28 163 TON 2 164 TTO 17 165 TUN 15 166 TUR 4 167 TZA 8 168 UGA 12 169 UKR 6 170 URY 36 171 USA 45 172 UZB 8 173 VAT 1 174 VEN 10 175 VNM 12 176 VUT 11 177 WSM 7 178 YEM 7 179 ZAF 17 180 ZMB 19 181 ZWE 22 [region] Region of the recipient country based on the UN Standard country or area codes for statistical use (M49) Region of the recipient country based on the UN Standard country or area codes for statistical use (M49) 2623 1 6 2.64 1.316 1 Africa 649 2 Asia 640 3 Europe 586 4 Latin America and the Caribbean 600 5 Northern America 57 6 Oceania 91 [subregion] Subregion of the recipient country based on the UN Standard country or area codes for statistical use (M49) Subregion of the recipient country based on the UN Standard country or area codes for statistical use (M49) 2623 1 22 13.07 6.227 1 Australia and New Zealand 6 2 Caribbean 112 3 Central America 149 4 Central Asia 36 5 Eastern Africa 195 6 Eastern Asia 107 7 Eastern Europe 130 8 Melanesia 60 9 Micronesia 14 10 Middle Africa 125 11 Northern Africa 59 12 Northern America 57 13 Northern Europe 64 14 Polynesia 11 15 South America 339 16 South-eastern Asia 300 17 Southern Africa 62 18 Southern Asia 111 19 Southern Europe 253 20 Western Africa 208 21 Western Asia 86 22 Western Europe 139 [donor] Donor name Donor name [donor_type] Donor type Donor type 2623 1 7 3.12 2.270 1 Party-state (ministries, state agencies, party organs/extensions, PLA, PAP, State-owned enterprises) 1160 2 Provincial level public/party entities 246 3 Municipal level public/party entities 182 4 University 15 5 (Non-state owned) enterprise 422 6 Foundation 373 7 Other 225 [region_origin] Indicates the region of origin of provincial and municipal level donors in the People's Republic of China. Marked only for donor types 2 and 3 Indicates the region of origin of provincial and municipal level donors in the People's Republic of China. Marked only for donor types 2 and 3 [recipient] Recipient name Recipient name [recipient_type] Recipient type Recipient type 2623 0 9 2.25 2.128 0 Unknown 81 1 Central/federal government 1478 2 State/provincial government 209 3 Municipal government 499 4 University 41 5 Enterprise 17 6 Individual 33 7 Foundation 86 8 Other 156 9 International/regional organization 23 [year] Donation year Donation year 2623 0 2021 2016.18 88.128 0 Unknown 5 2020 2547 2021 71 [month] Donation month Donation month 2623 0 12 4.98 2.152 0 Unknown 6 1 January 15 2 February 22 3 March 581 4 April 828 5 May 420 6 June 251 7 July 171 8 August 99 9 September 84 10 October 53 11 November 50 12 December 43 [day] Donation day. If more than one, first one is entered Donation day. If more than one, first one is entered 2623 0 31 16.18 8.837 [date1] Donation date, d/m/y. Donation date may refer for example to the day when the donation is dispatched from the donor, arrived to the country of destination or received by the recipient, depending on the information available Donation date, d/m/y. Donation date may refer for example to the day when the donation is dispatched from the donor, arrived to the country of destination or received by the recipient, depending on the information available 2617 11.02.2020 25.11.2021 26.05.2020 85 07:23:55.983 [week] Donation week. Counting from Monday Donation week. Counting from Monday 2623 0 53 20.38 9.243 [masks] The number of masks donated, surgical and other non-N95 types of masks The number of masks donated, surgical and other non-N95 types of masks 2623 0 9999999999 400415355.95 1960659827.486 0 945 50 1 100 7 140 1 150 1 200 4 300 3 332 1 333 1 400 2 500 9 600 3 667 1 750 1 1000 32 1150 1 1200 2 1500 8 1520 1 1550 1 1661 1 1666 3 1800 1 1900 1 2000 37 2040 1 2250 1 2325 1 2500 2 2750 1 2800 1 3000 18 3200 2 3325 1 3333 1 3375 1 3800 1 3875 4 4000 11 4166 6 4500 2 4650 2 5000 54 5250 2 5500 1 6000 11 6038 1 6500 1 6520 1 6650 1 6666 2 6668 1 6700 1 6750 9 7000 3 7100 2 7500 1 8000 10 9000 6 9083 1 9250 4 9286 1 9300 4 9500 1 9600 1 10000 151 10750 1 11000 4 11500 2 11625 2 11700 1 12000 18 12400 1 12450 1 12500 2 13000 1 13500 2 13800 1 14000 6 14050 1 14280 1 14625 1 14850 1 15000 15 15123 1 15750 2 16000 5 16167 1 16275 1 16666 2 17000 7 17500 1 17600 1 17666 1 17667 2 18000 41 19000 1 19163 1 19200 1 19350 1 19530 1 19744 1 19763 1 20000 106 20069 1 20500 2 20750 1 21000 2 21600 1 22000 2 22400 1 23000 2 23250 2 24000 1 25000 22 25200 1 25657 2 27000 3 27500 3 27900 1 28000 2 28125 1 28550 1 29000 1 30000 62 30200 1 30720 1 31000 1 31200 1 31500 1 32000 2 32500 4 32833 6 32970 1 33000 4 33333 3 33750 4 34000 3 34875 1 35000 3 35063 1 36000 2 38500 1 38750 1 39600 1 40000 25 40500 1 42000 2 42188 1 42383 1 44000 2 44333 3 44700 1 44875 1 45000 3 45600 1 46500 4 46666 2 46668 1 47000 1 48780 1 49600 1 49617 1 50000 114 50500 2 51100 2 51150 1 51315 37 52270 4 54000 2 54500 1 55000 2 56000 3 57625 1 58000 1 60000 15 62000 2 65000 2 65200 1 65250 1 66000 1 66666 4 66667 2 68000 1 70000 9 74400 1 74700 1 75000 4 75200 1 78000 1 79800 1 79900 1 80000 13 83600 1 85700 1 86666 5 86670 1 90000 2 90850 1 92300 1 93000 2 94000 1 97200 1 97500 2 98000 2 100000 152 100017 1 100800 2 101450 1 101500 1 102500 3 104000 1 104625 1 105000 2 110000 5 114750 1 115080 1 116667 3 119550 1 120000 5 120900 1 121500 1 124000 2 125000 3 126000 1 129000 2 130000 4 135000 1 137250 1 140000 2 150000 28 152000 1 157000 1 165000 1 166000 1 172000 1 175000 3 180000 1 181350 2 186000 1 189000 1 200000 34 201000 1 220000 3 240000 1 242857 7 250000 1 270000 1 279000 1 280000 1 283500 1 300000 20 330000 1 338000 1 339400 1 350000 4 370000 1 400000 9 430000 1 450000 1 460618 1 490000 1 500000 30 525000 1 550000 3 600000 4 680000 1 700000 2 722000 1 800000 1 838000 1 900000 1 982500 1 1000000 22 1058050 1 1070000 1 1185278 2 1200000 2 1202850 1 1300000 4 1400000 2 1500000 1 1885000 1 2000000 2 3000000 1 3750000 4 4600000 1 5000000 3 100000000 1 9999999999 Unspecified 105 [money] The number of money donation The number of money donation 2623 .00 30000000.00 33552.1106 759122.28861 [n95_masks] The number of N95/KN95 masks donated The number of N95/KN95 masks donated 2623 0 9999999999 114378648.37 1063521178.636 0 2124 40 1 80 1 100 2 120 1 180 1 200 4 250 3 300 6 418 1 458 4 500 6 600 1 800 2 1000 28 1200 4 1300 1 1500 5 1690 4 1857 1 2000 43 2100 1 2160 1 2283 1 2400 1 2500 8 2550 2 2857 1 3000 5 3004 1 3300 1 3380 2 3662 1 3718 1 4000 4 4333 6 4500 1 4800 2 4803 1 4914 1 5000 30 5040 2 5120 1 5277 1 5280 1 5520 1 5700 1 6000 4 6315 2 6866 2 6868 1 6880 1 7500 2 7875 4 8000 2 8500 1 8775 1 8793 1 9000 2 10000 36 10023 1 10320 1 10560 1 10563 1 10602 1 11000 1 11220 1 12000 2 12220 1 12300 1 12500 1 12625 2 12631 37 12700 1 13000 1 13333 3 13380 1 15000 16 16500 3 17000 1 18000 3 20000 29 20280 1 21000 2 21200 1 22500 1 22780 1 23000 3 24336 1 25000 3 27000 1 27020 1 28000 1 29760 1 30000 16 40000 4 42000 1 43000 1 50000 18 57686 1 65000 1 70000 5 72500 1 80000 1 100000 13 120000 1 124700 1 130000 1 150000 1 160000 2 180000 1 200000 3 250000 4 275000 4 300000 3 400000 1 560000 1 1000000 1 2000000 1 9999999999 Unspecified 30 [protective_clothing] The number of protective clothing donated The number of protective clothing donated 2623 0 9999999999 419369382.75 2004827431.705 0 1780 6 1 18 1 20 2 25 2 30 2 33 1 38 1 40 1 50 4 60 3 66 1 67 2 70 10 88 4 90 1 100 13 150 4 164 1 166 2 167 1 168 1 193 1 200 14 207 1 240 1 250 11 255 2 300 10 333 3 340 1 350 1 360 1 383 2 400 4 404 4 420 2 464 1 480 1 500 45 520 2 530 2 545 1 560 2 600 3 604 1 660 2 700 4 710 1 740 1 741 1 750 1 800 2 840 4 900 2 975 2 990 2 1000 91 1012 1 1033 1 1040 2 1100 2 1200 5 1210 1 1250 1 1253 1 1280 1 1297 1 1308 1 1313 2 1333 3 1350 1 1400 2 1404 1 1440 1 1450 1 1480 1 1500 7 1547 1 1600 1 1650 1 1666 2 1668 1 1740 2 1750 2 1800 3 1820 1 1830 1 1833 3 1848 1 1856 2 2000 56 2030 1 2040 1 2115 1 2227 1 2240 1 2330 1 2500 7 2520 2 2525 2 2700 1 2731 1 2897 1 3000 17 3333 2 3334 1 3360 2 3400 2 3480 3 3500 4 3550 1 3600 1 3666 2 3668 1 3700 7 3703 41 3750 1 3790 3 3800 2 3811 1 4000 2 4060 2 4112 1 4140 1 4200 2 4255 1 4400 1 4420 1 4480 1 4500 2 4700 1 4750 2 4970 1 5000 36 5166 2 5500 3 5544 1 5600 1 5800 1 5840 1 6000 6 6072 1 6367 2 6380 1 6720 1 6750 1 6860 1 6880 1 7000 4 7396 1 7500 5 7600 1 7940 2 8000 2 8120 1 9000 1 9100 1 9120 2 9450 1 9947 1 10000 37 12096 1 12458 1 12750 4 12975 1 13050 1 13090 1 14500 1 15000 9 15680 1 15960 1 16000 1 17500 1 18000 5 18500 1 19000 2 20000 11 20010 1 20160 1 21000 1 22000 1 23200 1 25000 1 29000 1 30000 7 35280 1 35480 1 50000 7 52000 1 54000 1 60000 2 61000 1 70000 3 78000 1 80000 2 87900 1 90000 1 100000 1 118000 1 147000 1 149688 1 200000 2 240000 1 668000 1 9999999999 Unspecified 110 [protective_clothing_type] General category. Includes protective clothing such as suits and gowns General category. Includes protective clothing such as suits and gowns [goggles] The number of goggles donated The number of goggles donated 2623 0 9999999999 190622013.81 1367695381.029 0 2309 4 1 20 3 30 1 33 1 50 9 61 1 66 2 67 2 70 10 75 1 100 9 120 1 125 4 167 1 200 3 266 1 287 1 300 4 357 1 399 2 400 1 460 1 480 2 486 1 500 18 510 1 585 1 589 2 600 3 700 1 750 4 764 1 765 1 773 1 798 1 800 1 840 1 866 1 900 3 1000 17 1064 1 1080 1 1100 1 1108 1 1200 1 1304 1 1320 1 1400 1 1500 4 1650 1 1800 1 1840 2 2000 33 2280 1 2420 1 2500 2 2525 2 2571 1 2944 1 3000 11 3024 1 3120 1 3260 1 3300 1 3722 1 3830 1 4140 1 4200 2 4530 1 4750 2 5000 7 6100 1 6500 1 7000 7 8648 1 9700 1 10000 13 10500 1 14500 1 15000 8 16279 1 17500 2 19000 1 23000 1 30000 8 35000 3 50000 2 54000 1 70000 3 110000 1 9999999999 Unspecified 50 [gloves] The number of gloves donated The number of gloves donated 2623 0 9999999999 148692536.30 1210494233.569 0 2250 20 1 50 1 200 3 400 1 500 13 600 2 950 1 1000 17 1100 1 1500 1 2000 15 2241 1 2679 1 2700 4 3250 1 3571 1 4000 5 4250 1 4800 1 5000 23 5200 2 5230 4 6000 4 7000 8 7200 2 7400 1 7500 2 8025 1 8450 1 9000 1 9259 42 9434 1 9500 10 9750 2 10000 41 12000 2 14563 1 15000 6 16000 1 16500 1 16800 1 17880 1 18000 1 19250 1 20000 6 21000 1 23760 1 25000 5 27225 1 28000 1 28500 1 29888 1 30000 6 31875 1 32500 2 37500 1 40000 3 45000 1 50000 14 53160 1 54000 1 55000 3 56150 1 60000 4 70000 2 75000 3 90000 1 100000 6 105000 2 106000 1 130000 1 150000 3 155520 1 160000 1 187000 1 200000 5 215700 1 219600 1 250000 1 300000 1 465000 2 498000 1 498750 4 500000 3 600000 1 700000 1 800000 1 930000 1 1000000 1 1500000 1 1924560 1 9999999999 Unspecified 39 [shoecovers] The number of disposable shoe covers donated The number of disposable shoe covers donated 2623 0 9999999999 26688549.24 516003129.275 0 2528 20 1 38 1 200 1 300 1 500 6 700 1 1000 8 1300 1 1500 1 1600 1 2000 2 3000 12 3500 1 4400 1 4643 1 5000 4 6000 1 9750 2 10000 24 11000 1 11500 1 25500 1 31400 1 35750 1 39500 1 40760 1 41650 1 50000 5 59167 1 120000 1 130000 1 383300 1 2530440 1 9999999999 Unspecified 7 [thermometers] The number of thermometers donated The number of thermometers donated 2623 0 9999999999 182996702.40 1340583100.841 0 2266 1 1 2 6 4 2 5 1 6 1 8 1 10 2 11 1 12 1 13 4 20 10 27 1 30 2 33 1 35 2 36 12 37 41 39 1 40 1 50 7 60 1 75 1 80 2 92 4 100 27 106 1 120 39 138 3 150 1 163 2 171 3 180 1 186 1 200 14 240 1 245 1 250 5 270 2 283 1 300 6 301 2 320 2 350 1 400 3 420 1 480 1 499 1 500 37 562 1 600 3 715 1 722 1 800 1 850 2 910 1 938 1 988 1 1000 9 1118 1 1200 1 1260 1 1272 1 1500 1 1625 1 1803 1 2000 3 2474 1 2670 1 2752 1 2968 1 3000 1 4311 1 7690 1 9425 1 9980 2 10000 1 10836 1 20000 1 37778 1 70000 2 9999999999 Unspecified 48 [visors] The number of visors donated The number of visors donated 2623 0 9999999999 53374357.00 728761720.983 0 2460 250 1 300 1 500 2 600 1 810 1 1000 54 1100 2 1500 2 1600 1 1603 1 1943 2 2000 3 2093 1 3000 6 3520 1 3600 1 3700 2 3703 41 3800 6 4000 1 4400 1 4933 1 5000 5 5010 1 10000 3 10600 1 20000 2 30000 2 120000 1 150000 1 200000 1 9999999999 Unspecified 14 [tests] The number of tests (/ test kits) donated The number of tests (/ test kits) donated 2623 0 9999999999 99127228.41 990847871.314 0 2167 2 1 50 1 71 1 100 1 200 1 240 1 250 1 300 2 400 1 417 1 500 11 625 3 661 1 750 2 834 2 1000 27 1008 4 1092 1 1200 3 1208 1 1250 6 1338 1 1365 1 1375 4 1500 5 1536 3 1666 1 1667 2 1733 1 1805 1 1850 1 2000 9 2016 3 2071 4 2074 1 2160 1 2400 1 2496 1 2500 8 2675 1 2827 1 2945 1 3000 5 4000 5 5000 16 5016 1 5040 2 5125 1 5358 1 5796 1 5920 2 6000 3 6800 1 7770 1 7863 1 8184 1 8908 1 9600 1 10000 29 10032 3 10080 2 10700 1 11000 1 11146 1 13032 1 13875 1 15000 5 17663 1 18500 1 18518 40 18700 1 18900 6 18912 5 19340 1 20000 79 20016 2 20064 1 20292 2 20350 1 20800 1 21000 1 22088 1 23307 1 23570 7 25000 6 30000 7 31683 1 33000 2 37320 1 37500 5 39119 1 40000 2 41625 1 49933 1 50000 13 50016 1 50040 1 60000 1 70000 1 74000 1 80000 1 100000 9 100008 1 145000 1 150000 6 162888 1 200000 1 500000 2 550000 1 1000000 1 9999999999 Unspecified 26 [ventilators] The number of ventilators donated The number of ventilators donated 2623 0 9999999999 110560428.71 1045847820.595 0 2333 1 35 2 59 3 8 4 10 5 30 6 2 7 4 8 6 9 39 10 9 12 3 13 1 15 4 17 1 18 2 20 9 21 1 22 2 23 2 25 3 26 1 30 7 35 2 36 1 38 1 40 2 50 6 64 1 80 1 90 1 100 2 130 1 144 1 150 1 200 1 300 1 1000 1 9999999999 Unspecified 29 [other_medical] Variable for other medical items Variable for other medical items [other_non_medical] Variable for other non-medical items, such as food donations and agricultural supplies Variable for other non-medical items, such as food donations and agricultural supplies [value_medicalaid_usd] Estimated value of the medical items and cash in US dollars Estimated value of the medical items and cash in US dollars 2623 .00 102000000.00 461476.7119 2834771.26208 [value_nonmedicalaid_usd] Estimated value of non-medical items in US dollars Estimated value of non-medical items in US dollars 2623 .00 7029983.00 6454.8706 169855.15931 [total_value_usd] The combined value of medical and non-medical aid US dollars The combined value of medical and non-medical aid US dollars 2623 .00 102000000.00 467931.5824 2838810.95064 [value_source] Indicates the source of the value estimate Indicates the source of the value estimate 2623 0 3 .69 .901 0 Calculation based on number of donated items and the reference prices 1593 1 Calculation based on box volumes and reference prices 267 2 Value indicated by the source 753 3 Combination of 0 and 2 10 [aid_type] Categorical variable dividing donations into medical, non-medical and mixed aid Categorical variable dividing donations into medical, non-medical and mixed aid 2623 0 2 .13 .427 0 Medical aid 2381 1 Non-medical aid 149 2 Both kinds of aid 93 [source] All the sources from which the information for the given donation comes All the sources from which the information for the given donation comes [notes] Additional remarks Additional remarks