cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/i9hmwe/ml4tjo
title: 29mar08_0015.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/o0vilx
title: 29mar08_0014.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/t7ikap
title: 29mar08_0013.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/uimhmb
title: 29mar08_0012.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/2uoq9s
title: 29mar08_0020.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/9ctb6c
title: 29mar08_0019.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/namuf7
title: 29mar08_0017.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/txegca
title: 29mar08_0016.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/v5vqpu
title: 29mar08_0018.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/wzjucb
title: 29mar08_0021.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/52ebur
title: 29mar08_0025.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/9a5vlh
title: 29mar08_0026.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/fvtsw4
title: 29mar08_0024.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/kcvteh
title: 29mar08_0022.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/rkzydl
title: 29mar08_0023.mat

description: :unav
DOI: 10.7910/dvn/sqyxuv/02v3bo
title: replication_log.log

description: :unav
DOI: 10.7910/dvn/sqyxuv/iw47nk
title: combined_database(STATA).tab

description: :unav
DOI: 10.7910/dvn/i9hmwe/l13ikv
title: 29mar08_0030.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/owytwm
title: 29mar08_0029.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/tsdohg
title: 29mar08_0031.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/xp70xi
title: 29mar08_0028.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/zgnjbr
title: 29mar08_0027.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/jgtwlr
title: 29mar08_0033.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/krq5bz
title: 29mar08_0034.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/nzdxcy
title: 29mar08_0032.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/wdksd3
title: 29mar08_0036.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/x3rnyt
title: 29mar08_0035.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/1cl24x
title: 29mar08_0039.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/dtcimb
title: 29mar08_0040.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/monzlk
title: 29mar08_0041.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/wiqbpj
title: 29mar08_0038.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/xtfucl
title: 29mar08_0037.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/0qpuyp
title: 29mar08_0042.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/f3hkdb
title: 29mar08_0045.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/ubbo9n
title: 29mar08_0044.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/uzyofu
title: 29mar08_0043.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/xujvln
title: 29mar08_0046.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/zn6pcg
title: 29mar08_0047.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/aad3un
title: 29mar08_0050.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/i5kmzs
title: 29mar08_0049.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/pqlssm
title: 29mar08_0052.mat

description: :unav
DOI: 10.7910/dvn/i9hmwe/t0lfot
title: 29mar08_0051.mat

description: :unav
DOI: 10.15468/dl.jucvpx
title: Occurrence Download

description: A dataset containing 164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Latimeria Smith, 1939"
 ]
}

 The dataset includes 164 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0094804-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wcsrwe
title: Occurrence Download

description: A dataset containing 6718 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is NHMO Mammal collection",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6718 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0426781-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avyf8n
title: Occurrence Download

description: A dataset containing 35589 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galeocerdo cuvier (Péron & Lesueur, 1822))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 35589 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0381341-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vp6aze
title: Occurrence Download

description: A dataset containing 26370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chorda filum (L.) Stackh.",
 "Year 2000-2023"
 ]
}

 The dataset includes 26370 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0100154-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7vetmf
title: Occurrence Download

description: A dataset containing 528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-69.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is CC-BY-NC 4.0",
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "TaxonKey is Euproctis chrysorrhoea Linnaeus, 1758"
 ]
}

 The dataset includes 528 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0201801-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7p63sg
title: Occurrence Download

description: A dataset containing 1001 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Mushroom (KIDR-MR)"
 ]
}

 The dataset includes 1001 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0172480-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s56nua
title: Occurrence Download

description: A dataset containing 366 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Megabalanus coccopoma (Darwin, 1854), Balanus tintinnabulum coccopoma Darwin, 1854)"
 ]
}

 The dataset includes 366 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0204436-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rdjn29
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Primula blattariformis Franch."
 ]
}

 The dataset includes 76 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0094798-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jgdmsv
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tulipa korolkowii Regel"
 ]
}

 The dataset includes 43 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0172485-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k593t9
title: Occurrence Download

description: A dataset containing 8410 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cyanocitta cristata (Linnaeus, 1758)",
 "Year 2011-2018"
 ]
}

 The dataset includes 8410 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0177330-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.759fe6
title: Occurrence Download

description: A dataset containing 3400749 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Juncus L."
 ]
}

 The dataset includes 3400749 records from 1957 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0205295-210914110416597/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.26327107
title: Update results

description: Updated meta-analysis results
DOI: 10.6084/m9.figshare.26327107.v1
title: Update results

description: Updated meta-analysis results
DOI: 10.15468/dl.hk3j4g
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cadia pubescens Bojer"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029180-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6075/j0mc907c
title: Predicting Mass Casualty Events from 911 Data Streams. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Predicting Mass Casualty Events From 911 Data Streams aims to provide dispatchers with a usable tool that alerts of any potentially occurring events while allowing them to monitor call conditions and real-time traffic alerts. The data involved was call data provided by the Communications Venture Corporation. This project was accomplished as part of the DSE MAS 24 program, for the class of DSE260.
DOI: 10.6075/j0736qsx
title: Prediction of Enzyme Classification using Protein Sequence Embeddings. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Biologists work with a multitude of protein sequences represented by strings of letters. The amino acid sequence of these proteins allows us to leverage various machine learning Natural Language Processing algorithms aimed to predict enzyme classifications which are indicative of both protein structure and functionality. Our goal is to propose a multi level classification solution that is designed to predict the respective class of a given enzyme. Our approach consists of predicting the classification of an enzyme by applying NLP to a protein sequence. Our method utilizes BERT (Bidirectional Encoder Representations from Transformers) models to create embeddings, or feature vectors, and a variety of machine learning models to predict the respective class and subclass of an enzyme.
DOI: 10.6075/j0ks6q2h
title: Protein Embedding Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Using deep learning transformer model BERT to study protein sequences and prediction tasks such as sub-cellular location, fluorescence, and secondary structure
DOI: 10.6075/j0891459
title: San Diego Housing Data Analysis - Part 2. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Predicting housing prices in San Diego County. The intended public benefit from the project is to provide further insights and guidance to homeowners, investors and government sectors. 
The baseline project went a long way in meeting this business challenge. Several important factors that determine the house values, such as regionality (costal, inland etc.), proximity of schools, seasonality and house size and stability in the San Diego county are built in the baseline model.
We intend to expand this to include:
Macroeconomic factors: mortgage rates
Microeconomic/regional factors, such as employment, migration, Crime
Our second challenge is to improve the housing price forecast models and hence accuracy of price prediction by 10% over baseline.
DOI: 10.6075/j0r49qz9
title: Seal Sleep Capstone. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: As part of the DSE MAS Data Science program, we constructed a project with the goal of to develop a machine learning framework capable of accurately classifying sleep states in Northern Elephant Seals. The data used included electrophysiological and motion data collected from the seals, comprising EEG (electroencephalogram) and ECG (electrocardiogram) signals, along with motion data from gyroscopes and pressure sensors. These data were gathered using custom non-invasive head caps and waterproof housings and were manually labeled for sleep, behavioral, and respiratory stages by experts. The raw signal data was provided in EDF format, and the labels were in CSV files with corresponding timestamps. This rich dataset enabled the extraction of key features necessary for training and evaluating the machine learning models.
DOI: 10.6075/j0kd1xsq
title: IOT Wearable Data Fever Analysis - COVID Detection. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: TemPredict launched in March 2020 as a collaboration between UCSF, UCSD, and Finnish wearable company Oura. The objective is to identify physiological signals from the wearable and provide early alerts for symptoms and diagnoses for COVID infection. The study's first phase ended on Nov 30th, 2020, with ~65,000 active participants. These participants shared data from their Oura ring from January 2020, answering onboarding, daily and monthly surveys about demographics, symptoms, and diagnoses, and other relevant information. Oura ring collects the person’s physiological data like heart rate, respiratory rate, skin temperature, metabolic equivalent of tasks. This data is stored, managed, and analyzed at SDSC. Our study focuses on developing an architecture that supports the different systematic exploration of approaches and performs comparison between them. We are analyzing the data, extracting new features, and building various algorithms that can be used for the early detection of COVID-19. In order to detect the onset of infection, we define a healthy window for each individual. This healthy window is derived by analyzing the daily rhythm of the physiological signals for every individual. In order to avoid false detection, the model calculates a dynamic baseline for each individual. Higher order features like ratios of temperature and activity, heart rate and its variability, deviations from the baseline, etc., are identified. Various ML models like Random Forest, XGBoost, CWT, Adaboost were trained, tested, and evaluated.
DOI: 10.6075/j0b56jx8
title: Trojan Attack Prediction. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: As machine learning (ML) has gained prominence in the business world, the implementation of deep neural networks (DNN) has become more widespread. The security of DNN models has recently come under scrutiny as they are at risk of adversarial attacks such as backdoor Trojan attacks. These attacks depend on a trigger to activate malicious behavior. Due to the lack of transparency in DNNs, the effects of Trojans may remain undetected until activated by an attacker. This project demonstrates a significant reduction in the time and resources necessary to detect a poisoned model through the use of dimensionality reduction techniques. The detector utilizes Principal Component Analysis and Independent Component Analysis to reduce model weights that can then be used to train a classification model. This work builds on previous research, integrating reduction techniques to significantly reduce inference time while maintaining model accuracy at 85%. Are you protected from malicious AI? 

Jacobs School of Engineering Data Science and Engineering Masters of Applied Science Program (DSE MAS) DSE 260 Capstone Project.
DOI: 10.6075/j0wm1br3
title: Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Objects in this collection are student-created project outputs and are not curated to the same extent as the rest of the research data in the Library's Digital Collections. This collection contains the capstone projects for the Data Science & Engineering (DSE) Master of Advanced Study (MAS) program at UC San Diego. Projects are completed over two years by groups of 2-5 students and are meant to be the culminating output of the program, providing experience with real-world data science work on large datasets (&gt;10 GB). Because the capstone focus is on workflow and analytics, data come from a variety of domains, including medicine, political science, and business. Each item in this collection includes, at minimum, the project's final report and code used to produce results. Input data and output data are included except in cases where data restrictions did not allow public sharing or data were already available on another stable repository.
DOI: 10.6075/j0pg1rzh
title: Debris Flow Risk Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Debris Flows are a distinct type of landslide that suddenly occur without warning. They are fast-moving channels of water and soil that carry large natural objects like boulders and trees, or human-made objects including cars. In the American West, Debris Flows have directly caused death and property damage. Debris Flows often occur after rain events and the burn scars left behind by wildfires increase their likelihood. Given the increasing frequency of extreme weather events, it is critical to predict Debris Flows and take precautionary action before they occur. This project builds upon prior research of predicting Debris Flows using additional geological features and more advanced machine learning techniques. The project also includes an intuitive interface for decision makers to access these probability estimates.
DOI: 10.5281/zenodo.8233974
title: Colombia. Demografia. 1998 2018. Municipio. Gestación. Gestantes.

description: Colombia. Demografia. 1998 2018. Municipio. Gestación. Gestantes.
DOI: 10.5281/zenodo.8233975
title: Colombia. Demografia. 1998 2018. Municipio. Gestación. Gestantes.

description: Colombia. Demografia. 1998 2018. Municipio. Gestación. Gestantes.
DOI: 10.15468/dl.umev8j
title: Occurrence Download

description: A dataset containing 998186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Benin",
 "Year 1996-2024"
 ]
}

 The dataset includes 998186 records from 328 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029176-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52kzna
title: Occurrence Download

description: A dataset containing 261 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Leopardus pardalis (Linnaeus, 1758)",
 "Year 1901-2000"
 ]
}

 The dataset includes 261 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029189-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ta642c
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Benin",
 "TaxonKey is Voacanga africana Stapf ex Scott Elliot",
 "Year 2000-2024"
 ]
}

 The dataset includes 66 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029190-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9phjje7
title: Sea Otter Aerial Survey Data from Western Prince William Sound, Alaska, 2022

description: This dataset consists of three tables related to abundance and distribution of northern sea otters (Enhydra lutris kenyoni) in western Prince William Sound, Alaska, based on data collected during a series of population-wide aerial surveys in June 2022. The dataset consists of: (1) sea otter counts along strip transects, (2) sea otter counts in Intensive Search Unit (ISU) within the transects, and (3) Transect coordinates.
DOI: 10.15468/dl.e9pap4
title: Occurrence Download

description: A dataset containing 1414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "OccurrenceStatus is Present",
 "TaxonKey is Mazama gouazoubira G.Fischer"
 ]
}

 The dataset includes 1414 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029192-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5j23ec
title: Occurrence Download

description: A dataset containing 470061 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alburnus alburnus (Linnaeus, 1758)"
 ]
}

 The dataset includes 470061 records from 394 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029186-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8hhcae
title: Occurrence Download

description: A dataset containing 5252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.65207 51.47486,-2.66039 51.46481,-2.6498 51.4685,-2.64867 51.46599,-2.65207 51.46434,-2.65043 51.46316,-2.64652 51.46426,-2.64451 51.46285,-2.64678 51.46096,-2.64199 51.45578,-2.644 51.45484,-2.6374 51.45262,-2.63064 51.45437,-2.62837 51.4513,-2.62648 51.45217,-2.62862 51.45539,-2.63089 51.45782,-2.6319 51.46246,-2.63379 51.46685,-2.63946 51.46882,-2.64514 51.47031,-2.6493 51.47251,-2.65207 51.47486))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 5252 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029193-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8227286
title: Justicia. America, Sur . 2012 2021. Correlacción Contexto BM. Indice de Justicia, IMJ. Indice de Paz Positiva, IPP

description: Justicia. America, Sur . 2012 2021. Correlacción Contexto BM. Indice de Justicia, IMJ. Indice de Paz Positiva, IPP
DOI: 10.5281/zenodo.8227287
title: Justicia. America, Sur . 2012 2021. Correlacción Contexto BM. Indice de Justicia, IMJ. Indice de Paz Positiva, IPP

description: Justicia. America, Sur . 2012 2021. Correlacción Contexto BM. Indice de Justicia, IMJ. Indice de Paz Positiva, IPP