Welcome to the Open South Developer API documentation!
\nWe are excited to provide you with access to our public API, enabling you to utilize it in various ways to enhance your applications. To get started, you'll need an API key, which you can find on the developer page of your dashboard.
\nOur API offers a range of endpoints designed to help you seamlessly integrate our services into your projects. Whether you're looking to access data, automate processes, or build new features, our API provides the flexibility and power you need.
\nPlease refer to the sections below for detailed information on how to access endpoints and handle responses.
\nHappy coding!
\n","schema":"https://schema.getpostman.com/json/collection/v2.0.0/collection.json","toc":[],"owner":"23812062","collectionId":"a91cdbf7-d573-4cde-8cd9-c7db66e92471","publishedId":"2sA3dyjBdN","public":true,"customColor":{"top-bar":"FFFFFF","right-sidebar":"303030","highlight":"FF6C37"},"publishDate":"2024-07-05T14:28:28.000Z"},"item":[{"name":"Get datasets","id":"8881bade-f64d-4780-83f8-356fe0985c9b","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"{{API_KEY}}"}]},"isInherited":false},"method":"GET","header":[],"url":"https://portal.opensouth.io/v1/api/dataset/?lang=en&limit=15&offset=0","description":"This endpoint retrieves a list of datasets with the specified parameters.
\nMethod: GET
\nBase URL: https://portal.opensouth.io
Path: /v1/api/dataset/
Query Parameters:
\nlang (string, required): The language for the dataset information.
\nlimit (integer, required): The maximum number of datasets to retrieve.
\noffset (integer, required): The offset for paginating through the datasets.
\nThe response for this request follows the JSON schema below:
\n{\n \"type\": \"object\",\n \"properties\": {\n \"count\": {\n \"type\": \"integer\"\n },\n \"next\": {\n \"type\": [\"string\", \"null\"]\n },\n \"previous\": {\n \"type\": [\"string\", \"null\"]\n },\n \"results\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"string\"\n },\n \"title\": {\n \"type\": \"string\"\n },\n \"slug\": {\n \"type\": \"string\"\n },\n \"license\": {\n \"type\": \"string\"\n },\n \"description\": {\n \"type\": \"string\"\n },\n \"dui\": {\n \"type\": [\"null\", \"object\"]\n },\n \"update_frequency\": {\n \"type\": \"string\"\n },\n \"files_count\": {\n \"type\": \"integer\"\n },\n \"temporal_coverage\": {\n \"type\": \"string\"\n },\n \"spatial_coverage\": {\n \"type\": \"string\"\n },\n \"created_at\": {\n \"type\": \"string\"\n },\n \"updated_at\": {\n \"type\": \"string\"\n },\n \"geojson\": {\n \"type\": \"object\",\n \"properties\": {\n \"country\": {\n \"type\": \"string\"\n },\n \"coordinates\": {\n \"type\": \"string\"\n }\n }\n },\n \"tags_data\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n }\n }\n }\n },\n \"category_\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"slug\": {\n \"type\": \"string\"\n }\n }\n },\n \"publisher_\": {\n \"type\": \"object\",\n \"properties\": {\n \"first_name\": {\n \"type\": \"string\"\n },\n \"last_name\": {\n \"type\": \"string\"\n },\n \"type\": {\n \"type\": \"string\"\n }\n }\n }\n }\n }\n }\n }\n}\n\nFinal set of data used for analysis and plots used in the debris interactions on coral reefs of Palk Bay manuscript.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"10-04-2017,14-04-2021\",\n \"spatial_coverage\": \"Fiji\",\n \"created_at\": \"2024-04-10T21:03:31.364018+01:00\",\n \"updated_at\": \"2024-07-14T14:00:24.639129+01:00\",\n \"geojson\": {\n \"country\": \"Fiji\",\n \"coordinates\": \"178.4406,-18.1456\"\n },\n \"tags_data\": [\n {\n \"name\": \"analysis\"\n }\n ],\n \"category_\": {\n \"name\": \"Environment\",\n \"slug\": \"environment\"\n },\n \"publisher_\": {\n \"first_name\": \"Momora\",\n \"last_name\": \"Felibg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"1c5b11f2-6733-4769-8aa1-0098efd3aea1\",\n \"title\": \"Rapid Conceptual Design Exploration and Prototyping with Generative AI\",\n \"slug\": \"rapid-conceptual-design-exploration-and-prototyping-with-generative-ai\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"The base milk frother dataset comprises 1089 images labeled as Page-X.png, where X represents the sample ID. Alongside these images, the dataset includes a CSV file named sketch_drawing.csv, containing fields for Image_ID (representing the image ID) and text (depicting the image description). While other dataset fields are irrelevant for this project's objectives, the focus remains on these key components.
Following the utilization of the sketch2prototype framework on the base dataset, an augmented dataset is generated. Each sample is organized within its directory, containing four generated images and a log file (dalle_response.json) documenting the prompts used for image generation. Additionally, the original milk frother sketch (original.png) utilized in generating the four images resides in the directory. Furthermore, the sketch_drawing.csv file persists within the folder, facilitating continuity in dataset management and analysis.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"16-04-2009,18-04-2023\",\n \"spatial_coverage\": \"Niger\",\n \"created_at\": \"2024-04-10T18:54:37.932553+01:00\",\n \"updated_at\": \"2024-07-12T22:04:35.196411+01:00\",\n \"geojson\": {\n \"country\": \"Niger\",\n \"coordinates\": \"9.39764774206789,17.4261488452015\"\n },\n \"tags_data\": [\n {\n \"name\": \"Engineering Design\"\n }\n ],\n \"category_\": {\n \"name\": \"Energy\",\n \"slug\": \"energy\"\n },\n \"publisher_\": {\n \"first_name\": \"Darem\",\n \"last_name\": \"Rartg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"b47134a3-27a6-4205-95ff-873c774983db\",\n \"title\": \"Testing Validity for Diagnosing Intramammary Infection in Dairy Cows at Dry-Off\",\n \"slug\": \"testing-validity-for-diagnosing-intramammary-infection-in-dairy-cows-at-dry-off\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"The objective of this cross-sectional study was to estimate the validity of laboratory culture, Petrifilm and Tri-Plate on-farm culture systems, and luminometry to correctly identify intramammary infection (IMI) at dry-off in dairy cows, considering all tests as imperfect. This dataset includes the anonymized data and the R code used, and a Table (S1) and a Figure (S1) to complement the results presented in the manuscript.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"06-04-2022,16-09-2022\",\n \"spatial_coverage\": \"Bangladesh\",\n \"created_at\": \"2024-04-10T17:34:50.736740+01:00\",\n \"updated_at\": \"2024-07-12T15:54:23.790682+01:00\",\n \"geojson\": {\n \"country\": \"Bangladesh\",\n \"coordinates\": \"90.0779740421191,24.3476077999364\"\n },\n \"tags_data\": [\n {\n \"name\": \"Infection\"\n },\n {\n \"name\": \"cows\"\n }\n ],\n \"category_\": {\n \"name\": \"Agriculture\",\n \"slug\": \"agriculture\"\n },\n \"publisher_\": {\n \"first_name\": \"Sohosep\",\n \"last_name\": \"Etopys\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"5e51e4bb-5beb-4462-ae7a-831b040af451\",\n \"title\": \"The Prospects and Constraints of Climate Migrants\",\n \"slug\": \"the-prospects-and-constraints-of-climate-migrants\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"What is the effect of environmental hazards on individual migration decisions when climate change-related mobility is permitted by a foreign state? We leverage a full immersion, qualitative case study and an original, large-scale survey of Chuukese citizens in the Federated States of Micronesia, a unique setting highly vulnerable to climate change and with an exceptional treaty granting FSM citizens access to the United States as non-immigrants without visas. We find that environmental hazards do not play a direct role in Chuukese decisions to leave their islands, despite the pervasive awareness of these hazards. Our findings instead suggest that environmental risks contextualize migration decisions, but do not drive them. Other factors like work, health, and family obligations take precedent.
\",\n \"dui\": null,\n \"update_frequency\": \"Annual\",\n \"files_count\": 1,\n \"temporal_coverage\": \"04-04-2023,04-04-2024\",\n \"spatial_coverage\": \"Morocco\",\n \"created_at\": \"2024-04-10T17:23:08.425834+01:00\",\n \"updated_at\": \"2024-07-11T16:29:50.881326+01:00\",\n \"geojson\": {\n \"country\": \"Morocco\",\n \"coordinates\": \"-6.01707272732872,32.3507513130338\"\n },\n \"tags_data\": [\n {\n \"name\": \"climate\"\n }\n ],\n \"category_\": {\n \"name\": \"Social Services\",\n \"slug\": \"social-services\"\n },\n \"publisher_\": {\n \"first_name\": \"Sevetal\",\n \"last_name\": \"Kravify\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"304f7535-99a4-4a74-ab83-ba59e9ac659e\",\n \"title\": \"Datasets on COVID-19 Vaccine Delivery Costs in the Democratic Republic of the Congo\",\n \"slug\": \"datasets-on-covid-19-vaccine-delivery-costs-in-the-democratic-republic-of-the-congo\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"This study forms part of a broader multi-country research endeavor, employing standardized methodologies to generate cost-related insights into the administration of COVID-19 (C19) vaccines across various regions. Specifically, the research spans Vietnam, Bangladesh, and the Philippines in Asia, and Mozambique, Côte d’Ivoire, the Democratic Republic of the Congo, and Uganda in Africa.
Conducted retrospectively, this bottom-up costing study delves into estimating both financial and economic costs associated with delivering C19 vaccines within the Democratic Republic of the Congo (DRC), encompassing routine as well as campaign-based delivery approaches. The study timeline covers various periods from November 2021 to June 2022, during which different sites were active.
From the payer perspective, the study encompasses costs incurred by a range of stakeholders, including health service providers, the Ministry of Public Health, Hygiene and Prevention's immunization program, and development partners, operating at all levels of the health system.
Data collection occurred retrospectively across a sample of 26 health facilities spread across the provinces of Kinshasa, Haut-Katanga, and Kongo Central. Additionally, relevant health zone, provincial, and national-level offices, along with four development partner organizations, were involved in the data collection process.
To gain insights into cost drivers, costs were meticulously disaggregated across program activities and resource types. Subsequently, volume-weighted average unit costs were calculated for each administrative level and then aggregated to determine the overall volume-weighted cost per dose.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"01-11-2021,30-06-2022\",\n \"spatial_coverage\": \"Democratic Republic of the Congo\",\n \"created_at\": \"2024-04-10T17:06:33.241934+01:00\",\n \"updated_at\": \"2024-07-12T17:14:52.441757+01:00\",\n \"geojson\": {\n \"country\": \"Democratic Republic of the Congo\",\n \"coordinates\": \"23.1668993658996,-1.17291850148401\"\n },\n \"tags_data\": [\n {\n \"name\": \"Immunization delivery costs\"\n },\n {\n \"name\": \"DRC\"\n },\n {\n \"name\": \"Vaccine delivery costs\"\n },\n {\n \"name\": \"COVID-19\"\n }\n ],\n \"category_\": {\n \"name\": \"Health\",\n \"slug\": \"health\"\n },\n \"publisher_\": {\n \"first_name\": \"Bogana\",\n \"last_name\": \"Rartg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"96e5cfd5-0c0b-48d1-8fde-89ef686accd6\",\n \"title\": \"Measurement of health human capital and its economic effect in China\",\n \"slug\": \"measurement-of-health-human-capital-and-its-economic-effect-in-china\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"This dataset provides the basic data and calculation process of the health human capital index, as well as the regression estimation and regional heterogeneity analysis of the impact of health human capital on economic growth.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 2,\n \"temporal_coverage\": \"01-04-2024,10-04-2024\",\n \"spatial_coverage\": \"China\",\n \"created_at\": \"2024-04-10T15:51:21.632949+01:00\",\n \"updated_at\": \"2024-07-12T17:19:40.315874+01:00\",\n \"geojson\": {\n \"country\": \"China\",\n \"coordinates\": \"101.901875103385,35.4867029846329\"\n },\n \"tags_data\": [\n {\n \"name\": \"economy\"\n }\n ],\n \"category_\": {\n \"name\": \"Social Services\",\n \"slug\": \"social-services\"\n },\n \"publisher_\": {\n \"first_name\": \"Bogana\",\n \"last_name\": \"Rartg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"56293737-0352-4a12-b9a4-85728a58c565\",\n \"title\": \"Xenophobia Meter Dataset\",\n \"slug\": \"xenophobia-meter-dataset\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"The dataset of over 7,000 tweets labeled according to the 7-scale Xenophobia Meter from the 11 U.S.-based accounts that are on the forefront of defining the rhetoric related to immigration and policy.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 2,\n \"temporal_coverage\": \"24-04-2023,01-04-2024\",\n \"spatial_coverage\": \"Zimbabwe\",\n \"created_at\": \"2024-04-10T15:44:46.098127+01:00\",\n \"updated_at\": \"2024-07-12T17:13:56.538706+01:00\",\n \"geojson\": {\n \"country\": \"Zimbabwe\",\n \"coordinates\": \"29.9386693711454,-19.1894587635565\"\n },\n \"tags_data\": [\n {\n \"name\": \"xenophobia\"\n },\n {\n \"name\": \"twitter\"\n }\n ],\n \"category_\": {\n \"name\": \"Urbanization and Housing\",\n \"slug\": \"urbanization-and-housing\"\n },\n \"publisher_\": {\n \"first_name\": \"Vosog\",\n \"last_name\": \"Felibg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"5dbbef1b-9d7f-4186-85ac-084591b031ba\",\n \"title\": \"Behavioral experiment examining people's reactions to misinformation\",\n \"slug\": \"behavioral-experiment-examining-peoples-reactions-to-misinformation\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"The aggregrate results of an online behavioral experiment examining people's reactions to misinformation about individuals on social media.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"19-03-2024,10-04-2024\",\n \"spatial_coverage\": \"Mali\",\n \"created_at\": \"2024-04-10T04:57:04.575801+01:00\",\n \"updated_at\": \"2024-05-17T17:34:25.142588+01:00\",\n \"geojson\": {\n \"country\": \"Mali\",\n \"coordinates\": \"-1.25214497310038,17.3503352399376\"\n },\n \"tags_data\": [\n {\n \"name\": \"misinformation\"\n },\n {\n \"name\": \"social media\"\n },\n {\n \"name\": \"content moderation\"\n },\n {\n \"name\": \"defamation\"\n }\n ],\n \"category_\": {\n \"name\": \"Social Services\",\n \"slug\": \"social-services\"\n },\n \"publisher_\": {\n \"first_name\": \"Vosog\",\n \"last_name\": \"Felibg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"a57ecb05-bace-467b-97d2-7dcf96457460\",\n \"title\": \"Statistically downscaled and bias corrected climate time series for 1000 randomly selected CR2MET grid cells\",\n \"slug\": \"statistically-downscaled-and-bias-corrected-climate-time-series-for-1000-randomly-selected-cr2met-grid-cells\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"Database Overview: This database comprises time series data from 1000 grid cells situated in Continental Chile. These grid cells were meticulously selected to investigate the methodological decisions involved in mitigating biases and refining model projections for climate change impact assessments.
Data Source: The data originates from the CR2MET (v2.0) meteorological product, as documented by Boisier et al. (2018) and the Chilean Directorate General of Water (DGA) in 2022. These grid cells serve as a valuable resource for understanding climate dynamics and their implications.
Climate Clustering: To discern variations across different climates, we conduct a climate clustering analysis. By grouping similar regions, we gain insights into localized climate patterns and their potential influence on impact assessments.
Historical Precipitation Seasonality: Evaluating the capability of GCMs to replicate historically observed precipitation seasonality is crucial. We examine how this interplays with the choice of TS. To quantify performance, we compute the Taylor Skill Score (TSS), a valuable metric for assessing model accuracy.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 2,\n \"temporal_coverage\": \"10-04-2001,12-04-2004\",\n \"spatial_coverage\": \"Chile\",\n \"created_at\": \"2024-04-10T00:52:00.365316+01:00\",\n \"updated_at\": \"2024-04-10T23:48:00.904787+01:00\",\n \"geojson\": {\n \"country\": \"Chile\",\n \"coordinates\": \"-69.761008,-26.783346\"\n },\n \"tags_data\": [\n {\n \"name\": \"climate\"\n }\n ],\n \"category_\": {\n \"name\": \"Environment\",\n \"slug\": \"environment\"\n },\n \"publisher_\": {\n \"first_name\": \"Cofaje\",\n \"last_name\": \"Felibg\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"63677537-7925-4349-b883-46988f604b9f\",\n \"title\": \"Top Biogas Biomethane Potential Municipalities in Brazil 2020\",\n \"slug\": \"top-biogas-biomethane-potential-municipalities-in-brazil-2020\",\n \"license\": \"Creative Commons Attribution (CC BY)\",\n \"description\": \"Biogas production potential from the sugarcane agribusiness by municipality in Brazil in 2020
\",\n \"dui\": null,\n \"update_frequency\": \"Annual\",\n \"files_count\": 1,\n \"temporal_coverage\": \"03-04-2024,10-04-2024\",\n \"spatial_coverage\": \"Brazil\",\n \"created_at\": \"2024-04-10T00:31:05.057545+01:00\",\n \"updated_at\": \"2024-07-12T16:00:54.016183+01:00\",\n \"geojson\": {\n \"country\": \"Brazil\",\n \"coordinates\": \"-51.6197890205486,-9.58890301712257\"\n },\n \"tags_data\": [\n {\n \"name\": \"biomass\"\n }\n ],\n \"category_\": {\n \"name\": \"Agriculture\",\n \"slug\": \"agriculture\"\n },\n \"publisher_\": {\n \"first_name\": \"Himoha\",\n \"last_name\": \"Dacgu\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"4079fc2d-b2c7-43e9-9e56-fd47727e54a1\",\n \"title\": \"A cross-continental assessment of landscape approaches\",\n \"slug\": \"a-cross-continental-assessment-of-landscape-approaches\",\n \"license\": \"Creative Commons Attribution-NonCommercial (CC BY-NC)\",\n \"description\": \"This dataset describes 380 landscape approaches across three continents characterized with the same questionnaire. The data comes from three regional assessments Latin America and the Caribbean (Estrada-Carmona, Natalia, et al. 2014. “Integrated Landscape Management for Agriculture, Rural Livelihoods, and Ecosystem Conservation: An Assessment of Experience from Latin America and the Caribbean.” Landscape and Urban Planning 129:1–11); Africa (Milder, et al. 2014. “Integrated Landscape Initiatives for African Agriculture, Development, and Conservation: A Region-Wide Assessment.” World Development 54:68–80); and Asia (Zanzanaini, et al., 2017. “Integrated Landscape Initiatives for Agriculture , Livelihoods and Ecosystem Conservation : An Assessment of Experiences from South and Southeast Asia.” Landscape and Urban Planning 165:11–21). The dataset also includes a set of +23 landscape approaches characterized in 2021 through the CGIAR research network. The dataset includes two other tabs displaying the continuous and categorical variables significantly associated with each cluster of landscape approaches. Finally, the Metadata tab describes all the information provided in the dataset.
\",\n \"dui\": null,\n \"update_frequency\": \"Unknown\",\n \"files_count\": 1,\n \"temporal_coverage\": \"18-03-2024,10-04-2024\",\n \"spatial_coverage\": \"Indonesia\",\n \"created_at\": \"2024-04-10T00:11:40.653936+01:00\",\n \"updated_at\": \"2024-04-10T04:45:37.639900+01:00\",\n \"geojson\": {\n \"country\": \"Indonesia\",\n \"coordinates\": \"106.82721585,-6.17555357\"\n },\n \"tags_data\": [\n {\n \"name\": \"tropical forests\"\n },\n {\n \"name\": \"agricultural landscapes\"\n }\n ],\n \"category_\": {\n \"name\": \"Agriculture\",\n \"slug\": \"agriculture\"\n },\n \"publisher_\": {\n \"first_name\": \"Teyet\",\n \"last_name\": \"Dacgu\",\n \"type\": \"individual\"\n }\n },\n {\n \"id\": \"64ccb17a-5199-430d-bc14-4d9f23df10ba\",\n \"title\": \"Effectiveness of Biochar on Stormwater Infiltration in Upland and Nutrient Removal in Living Shoreline\",\n \"slug\": \"effectiveness-of-biochar-on-stormwater-infiltration-in-upland-and-nutrient-removal-in-living-shoreline\",\n \"license\": \"Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)\",\n \"description\": \"Delaware's Inland Bays face challenges due to excess of nutrients and considerable shoreline erosion. Additionally, stormwater runoff has emerged as a significant contributor to pollution to the bays, leading to compromised water quality for both aquatic ecosystems and recreational purposes. To challenge these issues, constructed living shorelines offers a natural approach to stabilize the shorelines, reduce erosion, and improve water quality. In our study, we applied biochar, a substance resembling charcoal produced from pyrolysis of wood waste, at two locations: an Upland area and a recently constructed Living Shoreline. We designated these locations as S1 and S2, respectively. Both S1 and S2 were divided into alternating treatment sections of 4% (w/w) biochar, and 0% (w/w) biochar (control). For S1, characterized by a 12-hour tidal cycle, the focus was on examining the impact of the biochar amendment on plant growth, nitrogen removal, and erosion reduction. The S2 site represents a meadow ecosystem where we planted a combination of native wildflowers and warm-season grasses. Unlike S1, S2 is unaffected by tidal cycles and the study aims to discern variations in infiltration rates with and without biochar. To our knowledge this is the first field application of biochar to a meadow ecosystem for studying the enhanced capture of overland flow.
\",\n \"dui\": null,\n \"update_frequency\": \"Bi-weekly\",\n \"files_count\": 1,\n \"temporal_coverage\": \"21-04-2005,15-04-2009\",\n \"spatial_coverage\": \"Liberia\",\n \"created_at\": \"2024-04-09T23:46:47.656123+01:00\",\n \"updated_at\": \"2024-07-10T01:17:48.275580+01:00\",\n \"geojson\": {\n \"country\": \"Liberia\",\n \"coordinates\": \"-9.30791350606419,6.44809185243873\"\n },\n \"tags_data\": [\n {\n \"name\": \"biology\"\n }\n ],\n \"category_\": {\n \"name\": \"Environment\",\n \"slug\": \"environment\"\n },\n \"publisher_\": {\n \"first_name\": \"Padari\",\n \"last_name\": \"Dacgu\",\n \"type\": \"individual\"\n }\n }\n ]\n}"}],"_postman_id":"8881bade-f64d-4780-83f8-356fe0985c9b"},{"name":"Get dataset files","id":"1aa45b6c-3c1d-4c3a-9a2b-9d0ca4aef47f","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"This endpoint retrieves a list of dataset files with optional pagination parameters.
\nMethod: GET
\nURL: https://portal.opensouth.io/v1/api/dataset/file/
Query Parameters:
\nkey (string, required): The unique key for accessing the dataset files.
\nlimit (integer, optional): The maximum number of files to retrieve.
\noffset (integer, optional): The offset for paginating through the files.
\nStatus: 200
\nContent-Type: application/json
\nBody:
\n {\n \"count\": 0,\n \"next\": null,\n \"previous\": null,\n \"results\": [\n {\n \"id\": \"\",\n \"file_name\": \"\",\n \"file_url\": \"\",\n \"format\": \"\",\n \"size\": \"\",\n \"sha256\": \"\",\n \"created_at\": \"\",\n \"updated_at\": \"\"\n }\n ]\n }\n\ncount (integer): The total count of dataset files.
\nnext (string): The URL for the next page of results, if available.
\nprevious (string): The URL for the previous page of results, if available.
\nresults (array): An array of dataset file objects containing their id, name, URL, format, size, SHA256 hash, creation timestamp, and last update timestamp.
\n