cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15139/s3/zoxqhs
title: Field Evaluation of Feasibility & Ease-of-Use of Novel Onchocerciasis Diagnostics - Mozambique

description: Background
Lack of sensitive, specific, point of care diagnostics are a major barrier to the collection of programmatic and research data on onchocerciasis. This project primarily aims to determine the accuracy of the two novel diagnostics, the GADx Ov16 RDT and the DDTD Ov16 and OvOC3261 biplex, by comparing each to the results from the existing SD Bioline Ov16 RDT, and skin snip quantitative polymerase chain reaction (qPCR), during an existing onchocerciasis survey in Mozambique. The aims of the study are:
• To determine the accuracy of the two novel diagnostics, by comparing each to the results from the existing SD Bioline Ov16 RDT, and skin snip qPCR.
• To assess cross reactivity of the two tests with other co-endemic helminth infections, specifically lymphatic filariasis, as assessed by the Filariasis Test Strip (FTS) test, and Mansonella, assessed by qPCR on dried blood spots (DBS).
• To use data from a. and b. to estimate sensitivity and sensitivity of the new tests in the Mozambican setting.
• To assess the usability of the novel tests by collecting data from the surveyors on their use and readability.
Methods
As part of the wider DISSECT research protocol, residents from two communities in the DISSECT study area were tested using an expanded diagnostic panel. All residents aged 5 years and older were eligible to participate. The diagnostics and samples collected for each individual were:
• Onchocerciasis screening: DDTD onchocerciasis RDT in the community; GADx onchocerciasis RDT in the community,60 ul whole blood for dried blood spot (DBS) for onchocerciasis SD bioline RDT screening in the lab.
• Mansonella screening: 60 ul whole blood for DBS to be used for Mansonella qPCR testing in the lab.
• Lymphatic filariasis screening: 75ul whole blood for lymphatic filariasis FTS test during the day, for anyone testing positive by FTS an additional 60ul night blood draw for DBS for LF qPCR.
Results discussion
In cases where there is no true gold standard diagnostic test, and no way to know who is truly infected or not, analyzing the performance of novel tests is difficult. Relative to the skin biopsy O-150 analysis, which is a definitive determinant of active infection, but with recognized limitations in sensitivity, the GADx test had 75.00% sensitivity and 77.52% specificity. DDTD recorded 64.29% sensitivity and 75% specificity when defining positivity as any line, but with the stricter criteria needing both lines the sensitivity is 7.14% and specificity 98.00%. However the WHO threshold for stopping MDA, and for OEM, make reference to Ov16 antibody prevalence, rather than skin snip positivity. The SD bioline RDT (lab) is one of the accepted methods of determining Ov16 prevalence. Relative to this test, the GADx had sensitivity of 82.14% and specificity of 98.18%. The DDTD had 77.78% sensitivity and 99.10 specificity.
DOI: 10.15468/dl.j3g8qa
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carpobrotus edulis",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 93 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012667-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.43wnbm
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carpobrotus edulis",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 137 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012668-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15139/s3/mp1ejx
title: Field Evaluation of Feasibility & Ease-of-Use of Novel Onchocerciasis Diagnostics - Ghana

description: Background: Onchocerciasis, causing severe itching, blindness, and economic hardship, is a target for elimination by the WHO. Accurate diagnostics are crucial for identifying areas of transmission and for stopping treatment. However, traditional diagnostics like skin-snipping and SD Ov16 ELISA tests have limitations, preventing their use as decision-making tools. This study evaluates the performance, feasibility, and ease of use of two new lateral flow assays (GADx Oncho test and DDTD multi-antigen test) for point-of-care (POC) and lab-based diagnosis, comparing them with existing SD Ov16 RDT and the skin snip PCR.
Methods: The study conducted head-to-head comparisons of the GADx and DDTD tests with the SD Ov16 RDT on whole blood in the field and dried blood spots in the lab. Participants 5 years and above were enrolled in the study. Field testing was implemented on whole blood collected from all participants in seven communities across different onchocerciasis-endemic regions in Ghana, and DBS samples were collected for laboratory testing. Skin snip samples were also collected from adults and tested in the laboratory using the O-150 qPCR. Blood samples were also tested for infections with Mansonella perstans using the qPCR, to assess any cross-reactivity with the tests. The sensitivity of the tests was assessed by comparing them with the SD and the skin-snip PCR data.
Results: Out of 1,700 enrolled participants, 1,696 participated in at least one of the tests. There was a substantial to near-perfect agreement between the results of the two tests, although the DDTD test identified more positives than the GADx test. However, the GADx test slightly outperformed the DDTD test in terms of sensitivity although the differences are not significant. For both RDTs, the tests run on eluted DBS in the lab provided the best results in terms of sensitivity. These tests meet the WHO DTAG TPP clinical sensitivity for mapping (&gt;60%) but not for stopping (&gt;89%). The skin snip qPCR returned only 26 positives, limiting the ability to undertake specificity assessments for the tests. The tests were easy to use in the field and in the lab. 
Conclusions/Next Steps: Both GADx and DDTD tests demonstrated promise as onchocerciasis diagnostics for mapping purposes but require further refinement to meet the thresholds for MDA-stopping decisions. Further detailed analysis is ongoing, combining the data from Ghana with that from other countries to understand the relative performance of the GADx and DDTD RDTs with respect to the SD test. The samples collected during the study have been stored to enable the re-evaluation of future versions of the tests.
DOI: 10.18434/mds2-4056
title: AFM Staircase Calibration
DOI: 10.21233/dd47-hc72
title: Varel Papierfabrik pollen dataset

description: Raw data for the Varel Papierfabrik pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.7910/dvn/4uukuq
title: Replication Data for: Politicians’ misinformation, its correction, and partisanship in Italy

description: Political misinformation represents a challenge to contemporary democracies. It is widely acknowledged that misinformation is not only spread by individual users on social media, but also by politicians employing both digital and legacy media to disseminate biased or misleading content to advance their political agendas. This study explores the mechanisms through which misinformed statements made by politicians influence public opinion and examines the effectiveness of corrections from academic/official sources or fact-checking websites, also focusing on the role of partisanship. We investigate whether agreement with a misinformed statement on key policy issues – minimum wage, COVID-19 vaccination, and working hours – increases when it is attributed to a politician, and whether corrections by academic/official sources or fact-checkers reduce agreement. Through survey experiments conducted in Italy, we find that while misinformation from politicians does not always affect agreement with false statements, corrections generally decrease agreement. However, partisanship plays a crucial role, with individuals more likely to resist correction when misinformation comes from politicians they have more positive feelings toward. These findings shed light on the complex relationship between misinformation, the effectiveness of corrective messages, and political identity in shaping public opinion.
description: Replication data for the article: "Quaranta M, Arrigoni LM. Politicians’ misinformation, its correction, and partisanship in Italy. Italian Political Science Review/Rivista Italiana di Scienza Politica. doi:10.1017/ipo.2025.10084"
DOI: 10.21233/7xcc-2d31
title: Kloster Barthe pollen dataset

description: Raw data for the Kloster Barthe pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.tnxfu9
title: Occurrence Download

description: A dataset containing 8551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carpobrotus edulis",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8551 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012669-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tcwy88
title: Occurrence Download

description: A dataset containing 936292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2010",
 "Month is April",
 "Day is greater than or equal to 22",
 "Day is less than or equal to 28"
 ]
}

 The dataset includes 936292 records from 362 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000608-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/3g6yxy
title: Replication Data for: Machines Do See Color: Using LLMs to Classify Overt and Covert Racism in Text

description: Code and data required to pre-train, fine-tune, and deploy a Transformers-based model trained to predict covert and overt racism in the Ecuadorian Twitter context. A description of the code and the order in which to run it can be found in the following github page: https://github.com/svallejovera/smr_racism.
DOI: 10.13130/rd_unimi/jmksaw/s8yzsl
title: ensemble_delta_ref_mono_vanilla_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N03_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/v2xqdy
title: ensemble_delta_ref_mono_oracle_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/2tvq8s
title: delta_self_ensemble_GROUND_TRUTH Sink N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/oyorth
title: ensemble_delta_ref_mono_vanilla_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/yddo3u
title: ensemble_quality_GROUND_TRUTH RR N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/qaw0dc
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/dojr5c
title: assignment_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/radvdc
title: assignment_TSUSC N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/pxvqwd
title: ensemble_delta_ref_mono_oracle_Distance.Boundary_max+RR N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/vupgh3
title: ensemble_quality_Distance.Boundary_max+RR N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/xh1fsw
title: ensemble_quality_Distance.Boundary_max+Sink N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/m2mo1g
title: ensemble_delta_ref_mono_vanilla_Distance.Boundary_max+RR N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jhtpsf
title: delta_self_ensemble_Distance.Boundary_max+Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/tqfzoe
title: delta_self_ensemble_Distance.Boundary_max+Sink N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/yqlqm8
title: ensemble_quality_Group.Auto+Neigh.kDistance_l2_k5_div+RR N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/yd3fdy
title: ensemble_quality_Distance.Boundary_max+Sink N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/bvx06m
title: ensemble_delta_ref_mono_oracle_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/cjyiwu
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/20assz
title: ensemble_delta_ref_mono_oracle_GROUND_TRUTH RR N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/yld1vm
title: ensemble_delta_ref_mono_oracle_Distance.Boundary_max+RR N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/cfw8p2
title: mono_vanilla_delta_self_training_set_clean.tab
DOI: 10.5281/zenodo.17634011
title: Underwater images collected by a Paddle in La-Preneuse, Mauritius - 2018-02-18

description: This dataset was collected by a Paddle in La-Preneuse, Mauritius - 2018-02-18. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.13130/rd_unimi/jmksaw/edpbsl
title: ensemble_delta_ref_mono_oracle_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/uftevh
title: ensemble_delta_ref_mono_vanilla_Group.Auto+Neigh.kDistance_l2_k5_div+RR N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ouxn7j
title: ensemble_delta_ref_mono_vanilla_Distance.Clustering_div+Sink N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/uwmemf
title: ensemble_delta_ref_mono_vanilla_GROUND_TRUTH RR N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/2syvvm
title: ensemble_quality_Distance.Boundary_max+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/frvgg7
title: delta_self_ensemble_GROUND_TRUTH Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/v6pcux
title: assignment_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ursaz5
title: assignment_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/demjft
title: ensemble_delta_ref_mono_oracle_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/dxgjby
title: assignment_Distance.Boundary_max+RR N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/mzbwck
title: ensemble_quality_Distance.Boundary_max+RR N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/uficjo
title: ensemble_quality_Distance.Clustering_div+RR N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/thfgui
title: ensemble_quality_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/j6nfp3
title: ensemble_quality_GROUND_TRUTH Sink N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/25ny9j
title: ensemble_delta_ref_mono_vanilla_GROUND_TRUTH Sink N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/iosalj
title: assignment_Distance.Boundary_max+RR N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/aie43f
title: delta_self_ensemble_Distance.Boundary_max+RR N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ouz9wu
title: delta_self_ensemble_GROUND_TRUTH Sink N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/duxxbf
title: assignment_GROUND_TRUTH Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/y11n22
title: ensemble_quality_GROUND_TRUTH Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/oe9xxl
title: assignment_Distance.Boundary_max+Sink N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/tc9oom
title: assignment_Distance.Boundary_max+RR N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/vghi9b
title: mono_vanilla_delta_ref_mono_oracle_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jhguld
title: assignment_Distance.Boundary_max+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/h4n1eu
title: ensemble_delta_ref_mono_vanilla_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/nlxyu6
title: ensemble_quality_TSUSC N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/wyrjxs
title: delta_self_ensemble_Distance.Clustering_div+Sink N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jiuveo
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+RR N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/gvp4pf
title: ensemble_delta_ref_mono_oracle_GROUND_TRUTH RR N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/denaqj
title: delta_self_ensemble_Distance.Clustering_div+RR N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/caylbn
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/gvrfnp
title: ensemble_quality_Distance.Clustering_div+Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/s42gjo
title: ensemble_quality_Distance.Boundary_max+Sink N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ljad14
title: ensemble_delta_ref_mono_vanilla_Distance.Clustering_div+RR N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/gxrztw
title: ensemble_delta_ref_mono_vanilla_TSUSC N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/lvpfhs
title: assignment_TSUSC N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/orudyr
title: ensemble_quality_Distance.Clustering_div+Sink N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/4vcv7e
title: ensemble_delta_ref_mono_oracle_GROUND_TRUTH Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/mbxfym
title: assignment_Distance.Boundary_max+RR N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/cvwqht
title: ensemble_delta_ref_mono_vanilla_GROUND_TRUTH Sink N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/s4pnao
title: ensemble_delta_ref_mono_vanilla_Distance.Clustering_div+RR N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/wbgtwa
title: ensemble_delta_ref_mono_oracle_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/z2ont2
title: ensemble_quality_GROUND_TRUTH Sink N33_training_set_clean.tab
DOI: 10.15468/dl.pdrbxx
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "StateProvince is California",
 "TaxonKey is Pacifastacus fortis"
 ]
}

 The dataset includes 37 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040013-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/jmksaw/ffh3hx
title: ensemble_delta_ref_mono_vanilla_Group.Auto+Neigh.kDistance_l2_k5_div+RR N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/0akbe0
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N03_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/pbn9kj
title: ensemble_delta_ref_mono_oracle_Distance.Clustering_div+Sink N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/fa3ogi
title: delta_self_ensemble_TSUSC N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/4recfq
title: ensemble_delta_ref_mono_vanilla_TSUSC N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/7cayen
title: ensemble_delta_ref_mono_oracle_Distance.Boundary_max+Sink N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/mytwcy
title: ensemble_delta_ref_mono_vanilla_TSUSC N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/rlfzw8
title: ensemble_quality_Distance.Boundary_max+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/m3xgo7
title: ensemble_quality_TSUSC N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/1jvf68
title: assignment_Distance.Boundary_max+RR N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/oh7r5h
title: delta_self_ensemble_Group.Auto+Neigh.kDistance_l2_k5_div+Sink N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/uqizia
title: ensemble_delta_ref_mono_oracle_Group.Auto+Neigh.kDistance_l2_k5_div+RR N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ftmz99
title: mono_vanilla_delta_self_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/k2hty7
title: delta_self_ensemble_Distance.Clustering_div+Sink N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ofmowb
title: assignment_Distance.Boundary_max+RR N03_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/wzftda
title: ensemble_quality_Distance.Boundary_max+Sink N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ndjnxx
title: ensemble_quality_GROUND_TRUTH RR N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/o3ugca
title: ensemble_quality_Group.Auto+Neigh.kDistance_l2_k5_div+RR N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jy4rpw
title: assignment_Distance.Boundary_max+Sink N03_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/gsh58y
title: ensemble_delta_ref_mono_vanilla_Distance.Clustering_div+Sink N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/9jlwxl
title: assignment_Group.Auto+Neigh.kDistance_l2_k5_div+RR N27_training_set_clean.tab