cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.t9yz2v
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paralithodes brevipes (H.Milne-Edwards & Lucas, 1841)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001902-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11588/data/glqg7z/2xczea
title: mtk_a01up2_leptismagna_texts_datadescription.txt
DOI: 10.15468/dl.s63pk3
title: Occurrence Download

description: A dataset containing 2236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Living Specimen, Occurrence evidence)",
 "TaxonKey is Eidolon helvum (Kerr, 1792)"
 ]
}

 The dataset includes 2236 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001903-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11588/data/glqg7z/iay3bl
title: mtk_a01up2_leptismagna_buildings.tab
DOI: 10.11588/data/glqg7z
title: Late Antique Statue Bases of Lepcis Magna

description: Interactive and searchable map of the city of Lepcis Magna, displaying the geographical distribution within the city scape of the late antique honorary and building inscriptions.

When translating a multi-faceted and multi-layered reality such as that of late antique Lepcis into a categorized environment, a certain level of simplification is needed, therefore reference to the written sources for detailed discussion remains indispensable (see I. Tantillo / F. Bigi, Leptis Magna. Una città e le sue iscrizioni in epoca tardoromana, Cassino 2010; as the map itself is a simplified rendering of the city and its buildings, please refer to existing bibliography for detailed maps, e.g. R. Bianchi Bandinelli / E. Vergara Caffarelli / G. Caputo, Leptis Magna, Milano 1964).

The map features all public inscriptions – published so far – produced in Lepcis in the period between 250 and 420 ad (six minor fragments with insufficient data have been left out: HD059617; HD059642; HD059661; HD067591; HD067593; HD059436). It also includes a series of four monuments whose inscription is now lost but which can be nonetheless assigned to the same period (B.L.T. = Bases with Lost Text). 

All the relevant data have been collected in a searchable database and grouped in ten fields.
DOI: 10.11588/data/glqg7z/gairwf
title: mtk_a01up2_leptismagna_texts.tab
DOI: 10.11588/data/0hjajs/pnir0r
title: sfb933_c05_erzaehlte_inschriften.tab

description: Kodierung UTF-8, Trennzeichen Komma
DOI: 10.11588/data/0hjajs/b3pvfj
title: sfb933_c05_erzaehlte_inschriften_beschreibung.tab

description: Kodierung UTF-8, Trennzeichen Komma
DOI: 10.11588/data/0hjajs/k6efzy
title: sfb933_c05_erzaehlte_inschriften_beschreibung.tab
DOI: 10.11588/data/0hjajs
title: Erzählte Inschriften in der Literatur des Mittelalters (Projektdatenbank)
title: Mit Beiträgen von Christiane Conrad von Heydendorff, Dennis Disselhoff, Christine Neufeld, Dennis Pulina und Katja Schulz.

description: &lt;p&gt;Diese Datenpublikation entstammt dem Teilprojekt C05 (»Inschriftlichkeit. Reflexionen materialer Textkultur in der Literatur des 12. bis 17. Jahrhunderts«) des Sonderforschungsbereichs 933 (»Materiale Textkulturen«, Förderzeitraum: 2011–2023). Im Rahmen des Teilprojekts wurden erzählte Inschriften in der mittelalterlichen Literatur gesammelt und auf einer Website öffentlich zugänglich gemacht. Diese Datenpublikation enthält den Datensatz, auf dem diese Website beruht. Das Projekt ist seit Ende 2023 abgeschlossen.&lt;/p&gt;

&lt;p&gt;Als Inschrift zählt Geschriebenes, das a) nicht zu den gängigen Formen von Schriftlichkeit gehört (also nicht mit üblichen Hilfsmitteln auf Pergament und Papier geschrieben ist) und/oder Geschriebenes, b) bei dem von einer intensiven Verbindung von Inhalt und Schriftträger auszugehen ist. Der Ausgangspunkt der Sammlung sind deutschsprachige erzählte Inschriften vor 1700. In der zweiten und dritten Förderperiode des SFB (2015–2023) wurden zudem verstärkt erzählte Inschriften in altnordischen, alt- und mittelenglischen, mittellateinischen und altfranzösischen Textstellen gesammelt.&lt;/p&gt;

&lt;p&gt;Belege für die englische und skandinavische Literatur des Mittelalters recht vollständig sein. Ebenfalls recht vollständig abgedeckt ist die deutschsprachige höfische Literatur des 12.-14. Jahrhunderts. Der Bereich der geistlichen und historiographischen Literatur wurde nicht systematisch durchgesehen.
Gesammelt wurden die Daten vom Projektleiter (Ludger Lieb), von den Projektmitarbeiter*innen (Frank Krabbes, Astrid Lembke, Michael R. Ott, Laura Velte, Philipp Friedhofen und Dennis Disselhoff), den studentischen Hilfskräften des Teilprojekts sowie von den Mercator Fellows (Gastwissenschaftler*innen): Katja Schulz (Altnordisch), Christine Neufeld (Alt- und Mittelenglisch), Christiane Conrad von Heydendorff (Altfranzösisch) und Dennis Pulina (Mittellatein). &lt;/p&gt;
description: Die gesammelten Textstellen entstammen im Wesentlichen gedruckten und online zugänglichen Editionen mittelalterlicher Texte.
DOI: 10.11588/data/0hjajs/ieltoh
title: sfb933_c05_erzaehlte_inschriften_20190626.csv
DOI: 10.11588/data/l5j7wo
title: 3D Focusing in Simple Microfluidic Devices for Continuous-flow Single-cell Imaging [data set]

description: Accompanying data for the manuscript "3D Focusing in Simple Microfluidic Devices for Continuous-flow Single-cell Imaging".
DOI: 10.11588/data/l5j7wo/bc4pt2
title: Supplementary File S3_budding yeast.tif

description: A stack of frames containing budding yeast cells extracted from an imaging flow cytometry run.
DOI: 10.11588/data/l5j7wo/chqrdi
title: Supplementary File S2_beads.tif

description: A stack of frames containing beads extracted from an imaging flow cytometry run.
DOI: 10.11588/data/l5j7wo/ld9res
title: Supplementary File S1_beads training set.tar.gz

description: Training images containing stationary beads for neural network training.
DOI: 10.11588/data/r8hgib/0ujsrp
title: Table3_text2.tab
DOI: 10.11588/data/r8hgib/30lxfi
title: Fig4.pdf
DOI: 10.11588/data/r8hgib/bkkf13
title: Table4_3.tab
DOI: 10.11588/data/r8hgib/d4xg9e
title: DGTV2018 variables.pdf
DOI: 10.11588/data/r8hgib/ekvcve
title: Table3_1237.tab
DOI: 10.11588/data/r8hgib/ixdvnp
title: Table3_fn.tab
DOI: 10.11588/data/r8hgib/kemwov
title: Table4_2.tab
DOI: 10.11588/data/r8hgib/l629l3
title: DGTV2018 analysis.do
DOI: 10.11588/data/r8hgib/owitbq
title: Table5.tab
DOI: 10.11588/data/r8hgib/rpunvv
title: Fig3.pdf
DOI: 10.11588/data/r8hgib/sxyc2z
title: Table4_1.tab
DOI: 10.11588/data/r8hgib/t71uhd
title: dengleretal_2018onlineappendix.docx

description: Online appendix with experimental instructions
DOI: 10.11588/data/r8hgib/vngleg
title: Table3_4568.tab
DOI: 10.11588/data/r8hgib/vpv3js
title: Fig2.pdf
DOI: 10.11588/data/r8hgib/wnyx3n
title: Table3_text1.tab
DOI: 10.11588/data/r8hgib/xlia4i
title: Fig1.pdf
DOI: 10.11588/data/r8hgib/z6akia
title: DGTV2018 raw data.tab
DOI: 10.11588/data/r8hgib
title: Climate policy commitment devices [Dataset]

description: We develop a dynamic resource extraction game that mimics the global multi-generation planning problem for climate change and fossil fuel extraction. We implement the game under different conditions in the laboratory. Compared to a baseline condition, we find that policy interventions that provide a costly commitment device or reduce climate threshold uncertainty reduce resource extraction. We also study two conditions to assess the underlying social preferences and the viability of ecological dictatorship. Our results suggest that climate change policies that focus on investments that lock the economy into carbon-free energy sources provide an important commitment device in the intertemporal cooperation problem.
DOI: 10.11588/data/r8hgib/aw3wd4
title: Table8_6.xlsx
DOI: 10.11588/data/r8hgib/e91crw
title: Table8_5.xlsx
DOI: 10.11588/data/r8hgib/lgiovi
title: Table6.tab
DOI: 10.11588/data/r8hgib/odzbfq
title: Table7.tab
DOI: 10.11588/data/r8hgib/v9wdkw
title: Table8_7.xlsx
DOI: 10.11588/data/r8hgib/zt6h58
title: Table8_1to4.xlsx
DOI: 10.11588/data/mm6zgu/pvhet0
title: MsLatFol258.zip

description: Musikbeispiele aus dem Musiktraktat "Musica cantus gregoriani" aus der Handschrift Ms. fol. lat. 258 (Staatsbibliothek zu Berlin - Preussischer Kulturbesitz) im MEI-Format
DOI: 10.11588/data/0hjw2a
title: Distance from a Distance: The Robustness of Psychological Distance Effects [Dataset]

description: Psychological distance effects have attracted the attention of behavioral economists in the context of descriptive modeling and behavioral policy. Indeed, psychological distance effects have been shown for an increasing number of domains and applications relevant to economic decision making. The current paper questions whether these effects are robust enough for economists to apply them to relevant policy questions. We demonstrate systematic replication failures for the distance-from-a-distance effect shown by Maglio et al. (2013, Journal of Experimental Psychology: General), and relate them to theoretical arguments suggesting that
psychological distance theories are currently too poorly specified to make predictions that are precise enough for economic analyses.
DOI: 10.11588/data/0hjw2a/0cva5j
title: experiment3_stata_data.tab

description: experiment 3
DOI: 10.11588/data/0hjw2a/4gv60r
title: experiment4_instructions_dutch.docx

description: experiment 4
DOI: 10.11588/data/0hjw2a/dg7pzb
title: experiment2_stata_do.do

description: experiment 2
DOI: 10.11588/data/0hjw2a/iuxiqe
title: experiment3_raw.tab

description: experiment 3
DOI: 10.11588/data/0hjw2a/ixskhq
title: experiment4_stata_data.tab

description: experiment 4
DOI: 10.11588/data/0hjw2a/kvq7be
title: experiment1_stata_do.do

description: experiment 1
DOI: 10.11588/data/0hjw2a/lfcu8i
title: experiment1_stata_data.tab

description: experiment 1
DOI: 10.11588/data/0hjw2a/ltjzau
title: experiment1_raw.tab

description: experiment 1
DOI: 10.11588/data/0hjw2a/ly0zoz
title: experiment2_raw.tab

description: experiment 2
DOI: 10.11588/data/0hjw2a/ohxgfs
title: experiment4_stata_do.do

description: experiment 4
DOI: 10.11588/data/0hjw2a/ufrhqn
title: experiment2_stata_data.tab

description: experiment 2
DOI: 10.11588/data/0hjw2a/xdarh9
title: experiment3_stata_do.do

description: experiment 3
DOI: 10.11588/data/fjq4xl
title: Selectional Preference Embeddings (EMNLP 2017)

description: &lt;p&gt;Joint embeddings of selectional preferences, words, and fine-grained entity types.&lt;/p&gt;

&lt;p&gt;The vocabulary consists of:
&lt;ul&gt;
&lt;li&gt; verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj"
&lt;li&gt; words and short noun phrases, e.g. "Titanic"
&lt;li&gt; fine-grained entity types using the FIGER inventory, e.g.: /product/ship or /person/politician
&lt;/ul&gt;
&lt;/p&gt;

&lt;p&gt;
The files are in word2vec binary format, which can be loaded in Python with gensim like this:

&lt;code&gt;
&lt;pre&gt;from gensim.models import KeyedVectors

emb_file = "/path/to/embedding_file"
emb = KeyedVectors.load_word2vec_format(emb_file, binary=True)&lt;/pre&gt;&lt;/code&gt;
&lt;/p&gt;
DOI: 10.11588/data/fjq4xl/e7za64
title: dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
unlemmatized, enhancedDependencies.
DOI: 10.11588/data/fjq4xl/erew2s
title: dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in binary word2vec format. unlemmatized, enhancedPlusPlusDependencies.
DOI: 10.11588/data/fjq4xl/gq9oso
title: dep.lemma.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in plain-text word2vec format. lemmatized, enhancedDependencies.
DOI: 10.11588/data/fjq4xl/ixp0hd
title: dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
unlemmatized, basicDependencies.
DOI: 10.11588/data/fjq4xl/jiwb4y
title: dep.lemma.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in plain-text word2vec format. lemmatized, enhancedPlusPlusDependencies.
DOI: 10.11588/data/fjq4xl/nteygg
title: dep.lemma.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in plain-text word2vec format. lemmatized, basicDependencies.
DOI: 10.11588/data/fjq4xl/rooltj
title: dep.lemma.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
lemmatized, basicDependencies.
DOI: 10.11588/data/fjq4xl/spioin
title: dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
unlemmatized, enhancedPlusPlusDependencies.
DOI: 10.11588/data/fjq4xl/uhik3w
title: dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in binary word2vec format. unlemmatized, basicDependencies.
DOI: 10.11588/data/fjq4xl/urjkpj
title: dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.txt

description: selectional preference embeddings in binary word2vec format. unlemmatized, enhancedDependencies.
DOI: 10.11588/data/fjq4xl/wzrcgq
title: dep.lemma.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
lemmatized, enhancedPlusPlusDependencies.
DOI: 10.11588/data/fjq4xl/z9z2dh
title: dep.lemma.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.bin

description: selectional preference embeddings in binary word2vec format.
lemmatized, enhancedDependencies.
DOI: 10.11588/data/v9cxpr
title: BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018)

description: BPEmb is a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE).
In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages better
than alternative subword approaches, while requiring vastly fewer resources and no tokenization.
description: This dataset is split into 275 archives, one for each language. Languages are identified via their Wikipedia ID, e.g. "en" for English or "de" for German. Each archives contains three kinds of files:


 *.w2v.txt: Pretrained embeddings in word2vec plain text format, for different vocabulary sizes (vs1000 to vs100000) and embedding dimensionalities (d25 to d300).
 *.vocab: The byte-pair vocabulary for the given vocabulary size (vs1000 to vs100000) in plain text format.
 *.model: A byte-pair segmentation model in binary SentencePiece format. *.model files contain the same information as *.vocab files and are provided for convenience.
DOI: 10.11588/data/9jkavw
title: Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"

description: This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis".

This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis:

&lt;ul&gt;
&lt;li&gt; chapter3.tar.gz: Java source code for the entity linking system based on interleaved multitasking, system results, system output. Java and Python source code for automatic verification of entity linking results. Java source code for the Visual Entity Explorer.
&lt;li&gt; chapter4.tar.gz: Java and Scala source code for extracting pairs of terms and their dependency context from GigaWord and Wikilinks.
&lt;li&gt; chapter5.tar.gz: Python code used to run entity typing experiments.
&lt;/ul&gt;
DOI: 10.11588/data/9jkavw/4nzm7c
title: chapter4.tar.gz

description: Files related to chapter 4
DOI: 10.11588/data/9jkavw/bsko3k
title: chapter3.tar.gz

description: Files related to chapter 3
DOI: 10.11588/data/9jkavw/ze4m4c
title: chapter5.tar.gz

description: Files related to chapter 5
DOI: 10.11588/data/lwn9xe
title: SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]

description: This repository contains code for reproducing experiments done in Marasovic and Frank (2018).&lt;p&gt;
&lt;p&gt;
&lt;b&gt;Paper abstract:&lt;/b&gt;&lt;p&gt;

For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towards what?". Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis, we determine what works and what might be done to make further improvements for ORL.&lt;p&gt;
&lt;p&gt;

&lt;b&gt;Data for ORL&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Download MPQA 2.0 corpus.
&lt;li&gt;Check mpqa2-pytools for example usage.
&lt;li&gt;Splits can be found in the datasplit folder.
&lt;/ul&gt;
&lt;p&gt;
&lt;b&gt;Data for SRL&lt;/b&gt;&lt;p&gt;
The data is provided by: CoNLL-2005 Shared Task, but the original words are from the Penn Treebank dataset, which is not publicly available.&lt;p&gt;

&lt;b&gt;How to train models?&lt;/b&gt;&lt;p&gt;

&lt;p&gt;
python main.py --adv_coef 0.0 --model fs --exp_setup_id new --n_layers_orl 0 --begin_fold 0 --end_fold 4&lt;p&gt;
&lt;p&gt;
python main.py --adv_coef 0.0 --model html --exp_setup_id new --n_layers_orl 1 --n_layers_shared 2 --begin_fold 0 --end_fold 4&lt;p&gt;
&lt;p&gt;
python main.py --adv_coef 0.0 --model sp --exp_setup_id new --n_layers_orl 3 --begin_fold 0 --end_fold 4&lt;p&gt;
&lt;p&gt;
python main.py --adv_coef 0.1 --model asp --exp_setup_id prior --n_layers_orl 3 --begin_fold 0 --end_fold 10&lt;p&gt;
DOI: 10.11588/data/lwn9xe/uvquff
title: SRL4ORL_NAACL_2018.zip
DOI: 10.11588/data/udmpy5
title: Abstract Anaphora Resolution [Source Code]

description: Abstract Anaphora Resolution (AAR) aims to find the interpretation of nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g., this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations. &lt;p&gt;
&lt;p&gt;
The folder Silver Data contains the code for processing the silver training data described in Marasović et al. (2017). For more information read Silver Data/README.&lt;p&gt;
&lt;p&gt;
The folder Gold Data contains the code for processing the gold training and evaluation data. Use Gold Data/process_aar_data.py to prepare the ASN corpus (Kolhatkar et al, 2013) and the CoNLL-12 shared task data (Jauhar et al, 2015). Read arrau_csn/instructions_arrau_construction.txt for processing of the ARRAU corpus (Poesio et al, 2018).&lt;p&gt;
&lt;p&gt;
The implementation for training and evaluating models presented in Marasović et al. (2017) maybe be found in the folder EMNLP 2017. The readme contains the information on how to run the training and evaluation scripts. &lt;p&gt;
&lt;p&gt;
The implementation for training and evaluating models presented in the thesis may be found in the Thesis folder. &lt;p&gt;
&lt;p&gt;
DOI: 10.11588/data/udmpy5/ge7ky4
title: AbstractAnaphoraResolution.zip
DOI: 10.11588/data/rr5bmf
title: Fully-automated quality assurance in multi-center studies using MRI phantom measurements [Dataset]

description: 43 measurements acquired in eight different sites within the 
&lt;a href="https://imagen-europe.com/"&gt;IMAGEN-project&lt;/a&gt;, comprising the following 3 T scanner types: Siemens Verio and TimTrio; General Electric Signa Excite, and Signa HDx; Philips Achieva. &lt;p&gt;
Additionally one phantom data set was aquired on a 3 T Siemens Skyra resulting in 44 measurements.
description: Matlab
DOI: 10.11588/data/rr5bmf/o8jsce
title: IMAGen_Phantom.zip
DOI: 10.11588/data/5rsam3
title: DCE-MRI of the human kidney using BLADE: A feasibility study in healthy volunteers [Dataset]

description: To evaluate the degree of motion compensation in the kidney using two different sampling methods, each in their optimized settings: A BLADE k-space acquisition technique and a routinely used kidney perfusion acquisition scheme (TurboFLASH).
Dynamic contrast enhanced magnetic resonance examinations were performed in 16 healthy volunteers on a 3 Tesla MR-system with two parameterizations of the BLADE sequence and the standard reference acquisition scheme. Signal intensity enhanced time curves were analyzed with a mathematical model and a widely published separable compartment model on cortex regions to assess robustness versus motion artifacts
DOI: 10.11588/data/5rsam3/yljncj
title: Blade.zip
DOI: 10.11588/data/qwss8l
title: Groups discipline resource use under scarcity [Dataset, Instructions, and Replication files]

description: Resource scarcity sharpens the conflict between short term gains and long term sustainability. Psychological research documents that decision makers focus on immediate needs under scarcity. While decision makers use available resources most effectively, they also borrow too much from future resources. Overall performance decreases as a consequence. Using an online experiment, we study how scarcity affects borrowing decisions in groups. We show that the negative effect of scarcity is weaker for groups than for individuals. Even in our minimal design that excludes direct interaction or communication, the fact that participants know that their own behavior affects another participant disciplines their use of scarce resources. Our results thus highlight the benefit of groups as units of human organization.
DOI: 10.11588/data/qwss8l/66ftdk
title: codebook_rd.combined.pdf

description: :unav
DOI: 10.11588/data/qwss8l/8uzfko
title: GS_Playerlevel_combined_20201127.tab

description: :unav
DOI: 10.11588/data/qwss8l/db9jzz
title: GS_Roundlevel_combined_20201127.tab

description: :unav
DOI: 10.11588/data/qwss8l/fdhj83
title: Instructions_GroupScarcity.pdf

description: :unav
DOI: 10.11588/data/qwss8l/gsjsow
title: RDD-GroupScarcity_20190205.pdf

description: :unav
DOI: 10.11588/data/qwss8l/hvha71
title: analysis_GS_20210409.R

description: Code that reproduces all analysis and figures in paper (in order of appearance)
DOI: 10.11588/data/qwss8l/kpawrt
title: codebook_d.combined.pdf

description: :unav
DOI: 10.11588/data/qwss8l/miufha
title: StudyPlan_GroupControl_20191202.pdf

description: :unav
DOI: 10.11588/data/qwss8l/zggas9
title: GS_logdata_combined.tab

description: :unav