cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/9rgx2z4s9b.2
title: Data for: "Insectivorous bats supplement echolocation with vision to hunt insects"

description: We examined whether bats use vision to enhance sensory redundancy when orienting and hunting small insects. 

We equipped 21 wild common noctule bats with high-resolution sensor loggers to measure their echolocation and movements when commuting and foraging in dark and lit environments. 
The experiments took place in Germany during summers of 2022 and 2023.

While commuting, the bats maintained consistent echolocation sampling across light levels. 
When tracking prey in illuminated environments, bats emitted calls less frequently and with slower decreases in call levels compared with dark conditions. 

We have uploaded the data in tables and code to plot the main figures of the manuscript:
"Insectivorous bats supplement echolocation with vision to hunt insects"

The data is uploaded in four different excel tables:

1. The two files with "Overall" in the file name includes data either per prey capture or pr 1 second commuting flights:

LightCommutingOverall.xlsx
LightPreycaptureOverall.xlsx


cues: the starting cue of the event. The duration of each row is 1 second both for commuting and for prey capture
fpk: The dominant stroke frequency measured in Hz - the mean wingbeat rate of that 1 second interval
q: A dimensionless value indicating the quality of the peak frequency (fpk). It is large if there is a clear spectral peak.
meanLux: Mean lux value for each 1 second interval
AnimalNo: Bat ID indicate the bat individual from 1 to 21.
WingbeatRate: Wingbeat rate rounded 
CallRate: Number of calls per 1 second interval
CallsPerWB: The mean number of calls emitted per wingbeat in the 1 second interval 
meanWBamp: The mean wingbeat amplitude per 1 second segment in m/s^2
tortuosity: The tortuosity index is 0 for straightline movement and 1 for extreme circular movement.

2. The two files with "Parameters" in the name is detailed echolocation and light data per call for either commuting segments or the prey captures

LightCommutingParameters.xlsx
LightPreyCaptureParameters.xlsx

C: Positions of each call measured in seconds into the 1 second commuting segments or prey captures. These call positions range from 0 to 1 corresponding to the length of each prey capture event of 1 second. 
time: Call positions measured (in seconds) as the time from prey capture or last commuting call in 1 sec segment
CI: Call interval of each call measured in ms. 
AOL: Apparent output level (dB re 20µPa^2s). Only useful for the prey capture events, since for the commuting data, all calls are clipped. 
meanLux: The mean lux level per 1 second segment
AnimalNo: Bat ID indicate the bat individual from 1 to 21.
TreatmentCluster: A threshold not being used

Only prey capture file:
count: Each prey capture event. All calls belonging to e.g. the first prey capture event has a count = 1. 
CiCluster: kmeans clustering (with 3 clusters) of call data according to the call intervals to group data into smaller segments.
DOI: 10.17632/9rgx2z4s9b.3
title: Data for: "Insectivorous bats supplement echolocation with vision to hunt insects"

description: We examined whether bats use vision to enhance sensory redundancy when orienting and hunting small insects. 

We equipped 21 wild common noctule bats with high-resolution sensor loggers to measure their echolocation and movements when commuting and foraging in dark and lit environments. 
The experiments took place in Germany during summers of 2022 and 2023.

While commuting, the bats maintained consistent echolocation sampling across light levels. 
When tracking prey in illuminated environments, bats emitted calls less frequently and with slower decreases in call levels compared with dark conditions. 

We have uploaded the data in tables and code to plot the main figures of the manuscript:
"Insectivorous bats supplement echolocation with vision to hunt insects"

The data is uploaded in four different excel tables:

1. The two files with "Overall" in the file name includes data either per prey capture or pr 1 second commuting flights:

LightCommutingOverall.xlsx
LightPreycaptureOverall.xlsx


timecues: the starting cue of each event. The duration of each row is 1 second both for commuting and for prey capture

WingbeatFrequency: The dominant stroke frequency measured in Hz - the 
mean wingbeat rate of that 1 second interval

meanLuxLevel: Mean lux value for each 1 second interval

Bat ID: Bat ID indicate the bat individual from 1 to 21.

meanWingBeatAmplitude: The mean wingbeat amplitude per 1 second segment in m/s^2

tortuosity: The tortuosity index is 0 for straightline movement and 1 for extreme circular movement.

2. The two files with "Parameters" in the name is detailed echolocation and light data per call for either commuting segments or the prey captures

LightCommutingParameters.xlsx
LightPreyCaptureParameters.xlsx

calltimecues: Positions of each call measured in seconds into the 1 second commuting segments or prey captures. These call positions range from 0 to 1 corresponding to the length of each prey capture event of 1 second. 

CallInterval: Call interval of each call measured in ms. 
meanLuxLevel: The mean lux level per 1 second segment

BatID: Bat ID indicate the bat individual from 1 to 21.
TreatmentCluster: A threshold not being used

Only prey capture file:
TimeBeforePreyCapture: Call positions measured (in seconds) as the time from prey capture or last commuting call in 1 sec segment

PreyCaptureNumber: Each prey capture event. All calls belonging to e.g. the first prey capture event has a count = 1. 

kMeansCluster: kmeans clustering (with 3 clusters) of call data according to the call intervals to group data into smaller segments. 

ApparentOutputLevel: Apparent output level (dB re 20µPa^2s). Only for the prey capture events, since for the commuting data, all calls are clipped. 

%% WavfilesExamples:
Folder with examples of prey captures from the 10 % darkest and brightest environments.
DOI: 10.17632/9rgx2z4s9b.4
title: Data for: "Insectivorous bats supplement echolocation with vision to hunt insects"

description: We examined whether bats use vision to enhance sensory redundancy when orienting and hunting small insects. 

We equipped 21 wild common noctule bats with high-resolution sensor loggers to measure their echolocation and movements when commuting and foraging in dark and lit environments. 
The experiments took place in Germany during summers of 2022 and 2023.

While commuting, the bats maintained consistent echolocation sampling across light levels. 
When tracking prey in illuminated environments, bats emitted calls less frequently and with higher call levels compared with dark conditions. 

We have uploaded the data in tables and code to plot the main figures of the manuscript.
Commuting flights:
Repository_table 1+3: Wingbeat amplitude (m/s^2) and frequency (Hz) (Table 1) and call interval (seconds) (table 3) per 1 second commuting flights along with categorical explanatory variables Bat ID (21 individuals) and LightorDark (0 = dark, 1 = light).

Repository_table 2: Wingbeat amplitude and frequency per 1 second foraging flights along with categorical explanatory variables Bat ID and LightorDark
Repository_table 4: Call interval (ms), source levels (dB re 20µPa^2s) according to time before prey capture (s) along with categorical explanatory variables Bat ID and LightorDark
Repository_table 5: Wingbeat frequency, amplitude and approach speed of the bat towards to the prey (m/s) along with categorical explanatory variables "Bat ID" and "LightorDark" and "Cluster": kmeans clustering (with 3 clusters) of call data according to the call intervals.

Repository_table 6: 10 % darkest and 10 % lightest prey capture data with call intervals, call levels and time before prey captures according to "LightorDark", "BatID" and "Cluster". 

%% WavfilesExamples:
Folder with examples of prey captures from the 10 % darkest and brightest environments.
DOI: 10.17632/cfwnrgd9jm
title: Child Mortality Risk Factor Dataset

description: Title: Child Mortality Risk Factor Dataset

Description: A dataset containing sociodemographic, maternal, and child health variables to predict child mortality (binary classification: 0 = survived, 1 = died).

Key Variables:

Demographics: Mother’s age, father’s age, residence (urban/rural).

Maternal/Child Health: Birth order, birth weight (kg), antenatal visits, institutional delivery (yes/no), vaccination status (yes/no), low birth interval (yes/no).

Socioeconomic Factors: Mother’s education level (no/primary/secondary/higher), wealth index (poor/middle/rich), access to water (yes/no), toilet facility (yes/no).

Target Variable: Mortality (0 or 1).B. Variable Descriptions
Add a table or list explaining each column:

Variable Type Description
mother_age Numerical Age of the mother in years.
father_age Numerical Age of the father in years.
birth_order Numerical Birth order of the child (e.g., 1 = firstborn).
birth_weight Numerical Birth weight in kilograms (kg).
mother_education Categorical Education level: No/Primary/Secondary/Higher.
wealth_index Categorical Socioeconomic status: Poor/Middle/Rich.
residence Categorical Urban or rural residence.
antenatal_visits Numerical Number of antenatal care visits during pregnancy.
institutional_delivery Binary Whether delivery occurred in a healthcare facility (Yes/No).
vaccination_status Binary Whether the child received vaccinations (Yes/No).
access_to_water Binary Access to clean water (Yes/No).
toilet_facility Binary Access to improved sanitation (Yes/No).
low_birth_interval Binary Short interval between pregnancies (Yes/No).
mortality Binary Target variable: 0 = survived, 1 = died.
Purpose: Predict child mortality risk using machine learning (e.g., logistic regression, decision trees, neural networks).

Keywords: Child mortality, predictive modeling, socioeconomic factors, maternal health, machine learning
DOI: 10.17632/cfwnrgd9jm.1
title: Child Mortality Risk Factor Dataset

description: Title: Child Mortality Risk Factor Dataset

Description: A dataset containing sociodemographic, maternal, and child health variables to predict child mortality (binary classification: 0 = survived, 1 = died).

Key Variables:

Demographics: Mother’s age, father’s age, residence (urban/rural).

Maternal/Child Health: Birth order, birth weight (kg), antenatal visits, institutional delivery (yes/no), vaccination status (yes/no), low birth interval (yes/no).

Socioeconomic Factors: Mother’s education level (no/primary/secondary/higher), wealth index (poor/middle/rich), access to water (yes/no), toilet facility (yes/no).

Target Variable: Mortality (0 or 1).B. Variable Descriptions
Add a table or list explaining each column:

Variable Type Description
mother_age Numerical Age of the mother in years.
father_age Numerical Age of the father in years.
birth_order Numerical Birth order of the child (e.g., 1 = firstborn).
birth_weight Numerical Birth weight in kilograms (kg).
mother_education Categorical Education level: No/Primary/Secondary/Higher.
wealth_index Categorical Socioeconomic status: Poor/Middle/Rich.
residence Categorical Urban or rural residence.
antenatal_visits Numerical Number of antenatal care visits during pregnancy.
institutional_delivery Binary Whether delivery occurred in a healthcare facility (Yes/No).
vaccination_status Binary Whether the child received vaccinations (Yes/No).
access_to_water Binary Access to clean water (Yes/No).
toilet_facility Binary Access to improved sanitation (Yes/No).
low_birth_interval Binary Short interval between pregnancies (Yes/No).
mortality Binary Target variable: 0 = survived, 1 = died.
Purpose: Predict child mortality risk using machine learning (e.g., logistic regression, decision trees, neural networks).

Keywords: Child mortality, predictive modeling, socioeconomic factors, maternal health, machine learning
DOI: 10.15468/dl.8pa2ym
title: Occurrence Download

description: A dataset containing 2988872 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dryomys nitedula (Pallas, 1778), Zicrona caerulea (Linnaeus, 1758), Thaumetopoea processionea Linnaeus, 1758, Glis glis (Linnaeus, 1766), Bubo bubo (Linnaeus, 1758), Anthophora furcata (Panzer, 1798), Arma custos (Fabricius, 1794), Picromerus bidens (Linnaeus, 1758), Dicranum montanum Hedw., Dendrocopus major, Osmoderma eremita (Scopoli, 1763), Cucujus cinnaberinus (Scopoli, 1763), Himantoglossum adriaticum H.Baumann, Rosalia alpina (Linnaeus, 1758), Tetrao urogallus Linnaeus, 1758, Allium ursinum L., Orchis pallens L., Cypripedium calceolus L., Trochulus hispidus (Linnaeus, 1758), Panorpa communis Linnaeus, 1758, Segestria senoculata (Linnaeus, 1758), Micrommata virescens (Clerck, 1757), Cepaea nemoralis (Linnaeus, 1758), Muscardinus avellanarius (Linnaeus, 1758), Phalangium opilio Linnaeus, 1758, Bibio marci (Linnaeus, 1758), Oryctes nasicornis (Linnaeus, 1758), Orchis purpurea Huds., Cervus elaphus Linnaeus, 1758, Laphria ephippium (Fabricius, 1777), Alinda biplicata (Montagu, 1803), Ciconia nigra (Linnaeus, 1758), Limodorum abortivum (L.) Sw., Galium odoratum (L.) Scop., Calamagrostis villosa (Chaix) J.F.Gmel., Sorbus domestica L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2988872 records from 6292 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007782-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gay9t6
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014820-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-4344
title: eSPARQL Implementation
title: Java Implementation based on Apache Jena

description: &lt;p&gt;This project contains the code of an implementation of the eSPARQL
language, which extends SPARQL-star with a `FROM BELIEF` that
allows an easy formulation of epistemic queries.&lt;/p&gt;

Further information can be found in the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=352686"&gt;README.md&lt;/a&gt; file.
DOI: 10.57703/ce-ca6e4
title: Smooth Surface A26
DOI: 10.15468/dl.eh3dxg
title: Occurrence Download

description: A dataset containing 902 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mangifera indica L."
 ]
}

 The dataset includes 902 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002781-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28882856
title: Dataset for Virus Surveillance (2021-2023), COVID Cases (2021-2023), Ct Values (2023) and meteorological Data (2023) in Arequipa

description: Dataset for Virus Surveillance (2021-2023), COVID Cases (2021-2023), Ct Values (2023), and meteorological Data (2023) in Arequipa.The CT Values sheet includes qRT-PCR-derived values for detecting Flu viruses (A and B), RSV, and SARS-COV-2 in the Arequipa (Peru) region.
DOI: 10.6084/m9.figshare.28882856.v1
title: Dataset for Virus Surveillance (2021-2023), COVID Cases (2021-2023), Ct Values (2023) and meteorological Data (2023) in Arequipa

description: Dataset for Virus Surveillance (2021-2023), COVID Cases (2021-2023), Ct Values (2023), and meteorological Data (2023) in Arequipa.The CT Values sheet includes qRT-PCR-derived values for detecting Flu viruses (A and B), RSV, and SARS-COV-2 in the Arequipa (Peru) region.
DOI: 10.13130/rd_unimi/jmksaw/kmugwr
title: delta_self_ensemble_Dist.Bound+Dist.Clust+Count+Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/yrtxab
title: ensemble_quality_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+RR N09_training_set_clean.tab
DOI: 10.5281/zenodo.17635708
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Brian Laurence Burtt, http://www.wikidata.org/entity/Q3286276. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q3286276
DOI: 10.13130/rd_unimi/jmksaw/ovtmtg
title: delta_self_ensemble_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+RR N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/74mjcb
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Group+Neigh.kDist+Max+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/e9aoyu
title: ensemble_quality_Dist.Bound+Group+Neigh.kDist+Max+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/kdet4a
title: ensemble_quality_Dist.Clust+Group+Neigh.kDist+Count+Sink N33_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/vjdkcm
title: ensemble_quality_Dist.Clust+Group+Neigh.kDist+Count+Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/kl88wm
title: mono_oracled_delta_self_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/elbp9t
title: assignment_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+Sink N15_test_set_clean.tab
DOI: 10.15468/dl.sqg6rf
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chionanthus pubescens"
 ]
}

 The dataset includes 166 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040096-251025141854904/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/f41twa
title: assignment_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+Sink N15_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/pek478
title: ensemble_quality_Dist.Bound+Group+Neigh.kDist+Count+RR N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ca8a16
title: assignment_Dist.Clust+Group+Neigh.kDist+Count+Sink N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/johroo
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+RR N03_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/pkjps3
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Dist.Clust+Max+RR N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/r3evlw
title: ensemble_delta_ref_mono_vanilla_Dist.Clust+Group+Neigh.kDist+Count+RR N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/kdkwkq
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Count+RR N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/0aox4w
title: assignment_Dist.Bound+Dist.Clust+Max+Sink N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/nwicq9
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Group+Neigh.kDist+Max+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/spnyi7
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+RR N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/kyhk1m
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+Sink N21_training_set_clean.tab
DOI: 10.15468/dl.hefm4s
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Munroa decumbens"
 ]
}

 The dataset includes 73 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040097-251025141854904/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/bxppd8
title: mono_oracled_quality_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/md4nfg
title: ensemble_quality_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+RR N03_test_set_clean.tab
DOI: 10.15468/dl.ddg6j6
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ilyoplax dentata"
 ]
}

 The dataset includes 28 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040099-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17635712
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Johann Gottlieb Otto Tepper, http://www.wikidata.org/entity/Q2891010. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q2891010
DOI: 10.13130/rd_unimi/jmksaw/sx0cl2
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+RR N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/pyd3xj
title: assignment_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/2fpknc
title: ensemble_quality_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+Sink N15_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/xcbifs
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Dist.Clust+Max+RR N21_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/oppkbd
title: ensemble_quality_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+RR N33_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/drm5lm
title: ensemble_quality_Dist.Bound+Dist.Clust+Max+RR N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/6p5gsn
title: ensemble_delta_ref_mono_vanilla_Dist.Clust+Group+Neigh.kDist+Count+Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/adddfm
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Max+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/ik9e9q
title: assignment_Dist.Clust+Group+Neigh.kDist+Max+Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/llyd5p
title: ensemble_quality_Dist.Bound+Group+Neigh.kDist+Max+Sink N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/9am9n6
title: assignment_Dist.Clust+Group+Neigh.kDist+Max+Sink N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/6c66je
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Count+RR N27_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jtwulh
title: ensemble_quality_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/mzlcd5
title: assignment_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/t2wcro
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Dist.Clust+Group+Neigh.kDist+Count+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/aawo2k
title: assignment_Dist.Bound+Group+Neigh.kDist+Count+Sink N03_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/1pjjxj
title: assignment_Dist.Bound+Group+Neigh.kDist+Count+Sink N09_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/alsxus
title: ensemble_delta_ref_mono_vanilla_Dist.Clust+Group+Neigh.kDist+Max+RR N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/h63l3t
title: delta_self_ensemble_Dist.Bound+Group+Neigh.kDist+Count+Sink N21_test_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/dcjkhq
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Max+Sink N09_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/cegrnr
title: ensemble_delta_ref_mono_vanilla_Dist.Bound+Dist.Clust+Count+Sink N27_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/tvvtu2
title: ensemble_delta_ref_mono_oracle_training_set_clean.tab
DOI: 10.13130/rd_unimi/jmksaw/jqqrjw
title: ensemble_delta_ref_mono_oracle_Dist.Bound+Group+Neigh.kDist+Count+Sink N21_training_set_clean.tab