cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vv7jcv
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis humilis"
 ]
}

 The dataset includes 20 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017174-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17529656
title: SUDEXP-TE2-high-BxB

description: This is the full set of data for the analysis of 9x9 Sudoku puzzles in the higher BxB part of the T&E(2) kingdom, via (1+BRT)-expansion of T&E(2) minimals with BxB ≥ 7, as explained in the book: BERTHIER Denis, Hierarchical Classifications in Constraint Satisfaction (Third Edition), Lulu Press, October 2025, ISBN 978-1-326-05648-3.
DOI: 10.5281/zenodo.17529657
title: SUDEXP-TE2-high-BxB

description: This is the full set of data for the analysis of 9x9 Sudoku puzzles in the higher BxB part of the T&E(2) kingdom, via (1+BRT)-expansion of T&E(2) minimals with BxB ≥ 7, as explained in the book: BERTHIER Denis, Hierarchical Classifications in Constraint Satisfaction (Third Edition), Lulu Press, October 2025, ISBN 978-1-326-05648-3.
DOI: 10.15468/dl.tm4xhp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetrastemma scutelliferum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017175-251025141854904/datasets/export for details.
DOI: 10.15468/dl.fyge49
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis convexa"
 ]
}

 The dataset includes 82 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017176-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xygqr3
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis rhombifolia"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017178-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rhh9kz
title: Occurrence Download

description: A dataset containing 566 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leopardus wiedii",
 "Country is Mexico",
 "Year is greater than or equal to 1700",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 566 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017181-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcck7s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetrastemma simplex",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017180-251025141854904/datasets/export for details.
DOI: 10.17605/osf.io/vb2nc
title: The Dataset -- A Five Task Model: From Cognition and Evolution to AGI

description: This project presents the Five Task Model: a scaffolded account of cognition that traces how living systems—from unicellular organisms to humans—adapt by solving five recurring informational tasks to keep Energy, Safety, and Reproduction (ESR) in balance. Rather than equating cognition with intelligence or specific organs, the model defines it functionally: organisms must recognize meaningful cues, interpret their significance in context, and choose a behavior that advances ESR. 

These tasks—binary discrimination, object interaction at a distance, manipulation of perception, group dynamics, and symbolic operation—map to five Basic Cognitive-Behavioral Structures (BCBS) that emerged sequentially through evolution and remain layered, producing a spectrum of cognitive capacities rather than a simple on/off property.

The framework reframes behavior change as the visible trace of underlying cognition and offers practical tools for psychology, education, and personality research. In particular, the Cognitive-Behavioral Load (CBL) Index profiles relative strengths and growth areas across the five controllers, supporting assessment, intervention design, and team composition.

Finally, the model clarifies the bridge “from cognition and evolution to AGI”: by reconstructing this layered control architecture on artificial carriers, we obtain principled guidance for designing human-like artificial agents—shifting the conversation from speculative breakthroughs to functional reconstruction grounded in life’s adaptive logic.

For detailed project documentation, see the README.md (https://osf.io/2mdqc) in the Files section.

The primary dataset is available here: https://doi.org/10.17605/OSF.IO/VB2NC
DOI: 10.15468/dl.as4ysh
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis dayaoshanioides"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017184-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rg4e8r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetrastemma subpellucidum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017185-251025141854904/datasets/export for details.
DOI: 10.21227/05xg-3828
title: "Instrumented Linux ELF Binaries"

description: "This archive contains prebuilt, coverage\u2011instrumented Linux x86\u201164 ELF binaries. One helper script (`_is_intrumented.sh`) is included for convenience; no harnesses, seeds, or other auxiliary tools are included."
DOI: 10.5281/zenodo.17349057
title: Quadrature squeezing in a nanophotonic microresonator

description: Data used in the manuscript "Quadrature squeezing in a nanophotonic microresonator".
DOI: 10.5281/zenodo.17349056
title: Quadrature squeezing in a nanophotonic microresonator

description: Data used in the manuscript "Quadrature squeezing in a nanophotonic microresonator".
DOI: 10.15468/dl.r7hcrn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetrastemma timofeevai",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017187-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17517632
title: Evaluated data from "Re-Assessing the Strength and Deformation Behavior of B2-ordered Binary Iron Aluminides"

description: The following file deisgnations are used:

Fe30Al = alloy with nominal 30 at.% Al and 70 at.% FeFe35Al = alloy with nominal 35 at.% Al and 65 at.% FeFe42Al = alloy with nominal 42 at.% Al and 58 at.% FeFe47Al = alloy with nominal 47 at.% Al and 53 at.% FeFe50Al = alloy with nominal 50 at.% Al and 50 at.% FeFe53Al = alloy with nominal 53 at.% Al and 47 at.% Fe

RT = Testing temperature was room temperature (20°C)400C = Testing temperature was 400°C500C = Testing temperature was 500°C600C = Testing temperature was 600°C700C = Testing temperature was 700°C

SH = Abbreviation for "strain hardening". The data presented in these files were processed by interpolation and smoothening.

HV = Abbreviation for "Vickers Hardness". Indentation load was 1kg, dwell time was 10s. At least ten indents were evaluated with grains of random orientation. Distance to indents was at least three times the largest diagonal of an indent, and distance to grain boundaries at least six times the largest diagonal of an indent.

NH = Abbreviation for "Nanohardness". A diamond Berkovich indenter was used, maximum indentation depth was 1 micron at a constant strain rate of 0.02 1/s. 9 indents wer performed for each grain. Distance between two indents was 30 micron. 

Homogenization = denotes the heat treatment state for testing, which is 1000 °C for 48h for all alloys except Fe53Al, where the duration was set to 168 h.

Annealed = denotes the heat treatment state for testing, which is 400°C for 120h for all alloys.

Further details are found in the preprint linked to this dataset.
DOI: 10.5281/zenodo.17517631
title: Evaluated data from "Re-Assessing the Strength and Deformation Behavior of B2-ordered Binary Iron Aluminides"

description: The following file deisgnations are used:

Fe30Al = alloy with nominal 30 at.% Al and 70 at.% FeFe35Al = alloy with nominal 35 at.% Al and 65 at.% FeFe42Al = alloy with nominal 42 at.% Al and 58 at.% FeFe47Al = alloy with nominal 47 at.% Al and 53 at.% FeFe50Al = alloy with nominal 50 at.% Al and 50 at.% FeFe53Al = alloy with nominal 53 at.% Al and 47 at.% Fe

RT = Testing temperature was room temperature (20°C)400C = Testing temperature was 400°C500C = Testing temperature was 500°C600C = Testing temperature was 600°C700C = Testing temperature was 700°C

SH = Abbreviation for "strain hardening". The data presented in these files were processed by interpolation and smoothening.

HV = Abbreviation for "Vickers Hardness". Indentation load was 1kg, dwell time was 10s. At least ten indents were evaluated with grains of random orientation. Distance to indents was at least three times the largest diagonal of an indent, and distance to grain boundaries at least six times the largest diagonal of an indent.

NH = Abbreviation for "Nanohardness". A diamond Berkovich indenter was used, maximum indentation depth was 1 micron at a constant strain rate of 0.02 1/s. 9 indents wer performed for each grain. Distance between two indents was 30 micron. 

Homogenization = denotes the heat treatment state for testing, which is 1000 °C for 48h for all alloys except Fe53Al, where the duration was set to 168 h.

Annealed = denotes the heat treatment state for testing, which is 400°C for 120h for all alloys.

Further details are found in the preprint linked to this dataset.
DOI: 10.15468/dl.knebad
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis pankaiyuae"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017188-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.663ers
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oreocharis dentata"
 ]
}

 The dataset includes 6 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017189-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18712/nsd-nsd3300-v1
title: NOKUT National Student Survey 2023, Institutions

description: The National Student Survey is an annual survey directed at university and college students, conducted by the Norwegian Agency for Quality Assurance in Education (NOKUT) on behalf of the Ministry of Education. The survey asks students about their views on various aspects related to study quality, including teaching, guidance, learning environment, learning outcomes, and other important topics. The students are asked if they would consider receiving additional questions at a later time.

The purpose of the survey is to provide a more comprehensive information base for individuals considering a study program and to contribute to quality development efforts at Norwegian universities and colleges. The results are published on a dedicated web portal and distributed through various reports. NOKUT also shares anonymized data with educational institutions and other relevant stakeholders to enable them to conduct their own analyses. Additionally, the data is made available for research through Sikt.

The National Student Survey 2023 consists of four files. This file contains information on the institutions.
DOI: 10.18712/nsd-nsd3301-v1
title: NOKUT National Student Survey 2023, Study Programmes

description: The National Student Survey is an annual survey directed at university and college students, conducted by the Norwegian Agency for Quality Assurance in Education (NOKUT) on behalf of the Ministry of Education. The survey asks students about their views on various aspects related to study quality, including teaching, guidance, learning environment, learning outcomes, and other important topics. The students are asked if they would consider receiving additional questions at a later time.

The purpose of the survey is to provide a more comprehensive information base for individuals considering a study program and to contribute to quality development efforts at Norwegian universities and colleges. The results are published on a dedicated web portal and distributed through various reports. NOKUT also shares anonymized data with educational institutions and other relevant stakeholders to enable them to conduct their own analyses. Additionally, the data is made available for research through Sikt.

The NOKUT National Student Survey 2023 consists of four data files. This file contains information on study programmes.
DOI: 10.5281/zenodo.17529703
title: ПЕРСПЕКТИВЫ ВНЕДРЕНИЯ ПРИНЦИПОВ «ЗЕЛЁНОЙ ЭКОНОМИКИ» В УЗБЕКИСТАНЕ

description: В статье анализируется современное состояние тенденций «зелёной экономики» в энергетическом секторе Узбекистана и определяются тенденции её развития. Рассматривается значение инвестиций в «зелёную экономику», в частности, возобновляемые источники энергии, для обеспечения устойчивого экономического роста.
DOI: 10.5281/zenodo.17529702
title: ПЕРСПЕКТИВЫ ВНЕДРЕНИЯ ПРИНЦИПОВ «ЗЕЛЁНОЙ ЭКОНОМИКИ» В УЗБЕКИСТАНЕ

description: В статье анализируется современное состояние тенденций «зелёной экономики» в энергетическом секторе Узбекистана и определяются тенденции её развития. Рассматривается значение инвестиций в «зелёную экономику», в частности, возобновляемые источники энергии, для обеспечения устойчивого экономического роста.
DOI: 10.15468/dl.gh6jzr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetrastemma tutus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017191-251025141854904/datasets/export for details.
DOI: 10.21227/vnht-gw12
title: "The snp file of the tunable low pass filter"

description: "The file can be used to establish the tunable filter."
DOI: 10.48579/pro/7xcckj
title: Replication Data for: Fig 7.1, Focal times, WTPDs, raw data, 04 Nov 2025-COPY

description: FILE DESCRIPTION Keywords: Focal times, white-tailed prairie dog, raw data Filename = Fig 7.1, Focal times, WTPDs, raw data, 04 Nov 2025 OLD Filename = WTPDs, focal times from 2012, 26 April 2022 All these focal times from spring 2012 This file checked by Julia Phillip in Fall 2021 I checked Julia's entries, and made only a few trivial changes on 26 April 2022 I did another quick check of this file in December 2021. And I have made a quick second check. For statistical analyses of focal samples, I used data from a single overall percentage for the entire field season calculated from single percentages of vigilance from focal samples per day per individual. WTPD marker = Nyanzol dye marker. No information from eartags in this file. Note, however, that all data are from a single year (2012). All data on WTPD focal times in this file were collected in 2012. Note, however, that all data for WTPD scan samples, were collected in 2007. These data used for Fig 7.1 in Hoogland 2026. This file ready for longterm storage on 30 May 2025. This file does NOT include data from focal samples from Hoogland 1979 = The effect of colony size on individual alertness of prairie dogs (Sciuridae: Cynomys spp.). Animal Behaviour 27:394-407. For Hoogland 1979, I used different methods than the ones for the data collected on WTPD focal times in 2012. Also, this file does NOT include information from Hoogland et al 2013 = Individual variation in vigilance among white-tailed prairie dogs (Cynomys leucurus). Southwestern Naturalist 58:279-285. Hoogland et al. 2013 has information from SCAN SAMPLES, and that information is in a different EXCEL file with the following title: Filename = Focal times, WTPDs, raw data, 30 May 2025 These data are in Hirschler IM, et al. 2016. What is the best way to estimate vigilance? A comparison of two methods for Gunnison's prairie dogs, Cynomys gunnisoni. Anim Behav. 121:117-122. Notice that this file is not sorted BY INDIVIDUAL WTPD. I did that to get the "Single estimate per individual of vigilance from focal samples = 8.75%+ 3.03 % (Mean + SD) depicted in Figure 7.1. This file thus contains ALL RAW DATA for WTPD focal samples for Fig 7.1. The raw data in this file comes from 50 different WTPDs. Note that N = 105 for SCAN SAMPLES. Sex = M = male, and Sex= 2 = female Filename = Fig 7.1, Focal times, WTPDs, raw data, 04 Nov 2025
DOI: 10.5281/zenodo.17529411
title: Underwater images collected by a Paddle in Prairie, Mauritius - 2017-11-18

description: This dataset was collected by a Paddle in Prairie, Mauritius - 2017-11-18. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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: 10.5281/zenodo.17529698
title: Underwater images collected by a Paddle in Prairie, Mauritius - 2017-11-18

description: This dataset was collected by a Paddle in Prairie, Mauritius - 2017-11-18. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!